• Proposes an Adversarial Batch Representation Augmentation for batch correction. • Models uncertainty of biological batch effects in representation learning. • Uses adversarial learning to identify challenges in the objective function. • Presents a synergistic optimization process for stable training. • Comprehensive experiments validate the effectiveness of the proposed method. High-Content Screening routinely generates massive volumes of cell painting images for phenotypic profiling. However, technical variations across experimental executions inevitably induce biological batch (bio-batch) effects. These cause covariate shifts and degrade the generalization of deep learning models on unseen data. Existing batch correction methods typically rely on additional prior knowledge (e.g., treatment or cell culture information) or struggle to generalize to unseen bio-batches. In this work, we frame bio-batch mitigation as a Domain Generalization (DG) problem and propose Adversarial Batch Representation Augmentation (ABRA). ABRA explicitly models batch-wise statistical fluctuations by parameterizing feature statistics as structured uncertainties. Through a min-max optimization framework, it actively synthesizes worst-case bio-batch perturbations in the representation space, guided by a strict angular geometric margin to preserve fine-grained class discriminability. To prevent representation collapse during this adversarial exploration, we introduce a synergistic distribution alignment objective. Extensive evaluations on the large-scale RxRx1 and RxRx1-WILDS benchmarks demonstrate that ABRA establishes a new state-of-the-art for siRNA perturbation classification.

The maintenance and management of traffic signal assets are crucial for ensuring the safety and smooth operation of road transportation. However, timely repairs and updates of damaged traffic signs remain a significant challenge due to the inefficiency of manual measurements. This study proposes an innovative system that automates traffic sign collection and assessment using deep learning techniques. The system performs three key tasks, including traffic sign detection and tracking, condition assessment, and inventory building, by processing dashboard camera videos with GPS data or open-source street imagery. The system achieves a$\text {mAP}_{50}$of 93.50% for detection and recognition and an accuracy of 93.56% for damage assessment by utilizing a retroreflectivity-related image feature extraction pipeline. The outputs are formalized into a visual inventory, enabling transportation agencies to manage traffic assets more efficiently. Field tests conducted in collaboration with the Washington State Department of Transportation validate the practical effectiveness of the framework. To further address the lack of publicly available condition datasets, this study also introduces the Damaged Traffic Sign Detection and Assessment (DTSDA) dataset, which combines real and synthetic images of damaged signs. Overall, the proposed approach offers a cost-effective and scalable alternative to the traditional labor-intensive method, thereby enhancing traffic asset management efficiency and contributing to safer transportation systems.

Dilemma zone (DZ) protection is vital for intersection safety, yet legacy detectors often lack the spatial resolution and responsiveness needed to track fast-changing vehicle dynamics. This paper presents DV-EISOS, an edge-deployed, vision-driven system for real-time DZ detection and adaptive signal control. Running on Jetson AGX Orin, DV-EISOS integrates YOLOv8 for vehicle detection, ByteTrack for multi-object tracking, homography-based mapping, and time-to-intersection logic to convert RTSP camera feeds into actionable kinematic metrics with millisecond-level latency. Through an NTCIP/SNMP interface, the system issues yellow-phase extensions only when approaching vehicles are assessed as high risk. A field deployment at a signalized intersection in Bellevue, WA demonstrated accurate speed estimation (RMSE < 2 mph), timely phase adaptation, and responses to ~10% of potential DZ events. These results indicate that edge AI can deliver scalable, low-latency safety control using existing cameras in real-world urban environments.

Trajectory prediction plays a critical role in connected and autonomous vehicles (CAVs), particularly in scenarios involving multiple interacting agents. In such dynamic settings, where CAVs and roadside units (RSUs) communicate to share observations and predictions, accurately forecasting the trajectories of all involved agents becomes even more critical. In this work, we introduce a communication-aware diffusion model that leverages collaborative communication among CAVs during inference. Unlike previous approaches that modify the training objective, our method applies the guidance only during inference. By adding three different communication-aware guidances, we reduce displacement errors and improve accuracy without modifying the training pipeline. We validate our method on the INTERACTION dataset and demonstrate better performance with the introduction of guidance in terms of ADE and FDE. Furthermore, our approach is modular and computationally efficient, making it particularly well-suited for real-time deployment in edge-assisted CAV systems where robust and low-latency predictions are critical. This work highlights how communication-aware guidance can bridge the gap between cutting-edge generative models and the system-level demands of multi-agent CAV applications.

Immunofluorescent (IF) imaging is crucial for visualising biomarker expressions, cell morphology and assessing the effects of drug treatments on sub-cellular components. IF imaging needs extra staining process and often requiring cell fixation, therefore it may also introduce artefacts and alter endogenous cell morphology. Some IF stains are expensive or not readily available hence hindering experiments. Recent diffusion models, which synthesise high-fidelity IF images from easy-to-acquire brightfield (BF) images, offer a promising solution but are hindered by training instability and slow inference times due to the noise diffusion process. This paper presents a novel method for the conditional synthesis of IF images directly from BF images along with cell segmentation masks. Our approach employs a Residual Diffusion process that enhances stability and significantly reduces inference time. We performed a critical evaluation against other image-to-image synthesis models, including UNets, GANs, and advanced diffusion models. Our model demonstrates significant improvements in image quality ( p < 0 . 05 in MSE, PSNR, and SSIM), inference speed (26 times faster than competing diffusion models), and accurate segmentation results for both nuclei and cell bodies (0.77 and 0.63 mean IOU for nuclei and cell true positives, respectively). This paper is a substantial advancement in the field, providing robust and efficient tools for cell image analysis. • We introduce a novel diffusion model to synthesise fluorescence images from brightfield images. • CellResDM improves quality, speed, and segmentation accuracy, surpassing existing models. • CellResDM model can simultaneously generates IF images and cell/nuclei segmentation.

In the realm of driving technologies, fully autonomous vehicles have not been widely adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing driving experiences. Among these, car-following behavior modeling plays a pivotal role, forming the foundation for systems that ensure safe and efficient vehicle interactions. However, current approaches often rely on fixed parameters, failing to capture the diverse social preferences and driving styles of individuals. To overcome these limitations, we propose the Editable Behavior Generation (EBG) model, a data-driven car-following model that allows for adjusting driving discourtesy levels. The framework integrates diverse courtesy calculation methods into long short-term memory (LSTM) and Transformer architectures, offering a comprehensive approach to capture nuanced driving dynamics. By integrating various discourtesy values during the training process, our model generates realistic agent trajectories with different levels of courtesy in car-following behavior. Experimental results on the naturalistic datasets showcase a reduction in Mean Squared Error (MSE) of spacing and MSE of speed compared to baselines, establishing style controllability. To the best of our knowledge, this work represents the first data-driven car-following model capable of dynamically adjusting discourtesy levels. Our model provides valuable insights for the development of ADAS that take into account drivers’ social preferences.

Driven by advances in artificial intelligence, deep reinforcement learning (DRL) has made remarkable strides in adaptive traffic signal control (ATSC), empowering improved handling of fluctuating traffic volumes and congestion. However, in most existing studies, trained agents exhibit poor transferability in scenarios with varying vehicle turning ratios, and the switching rules for the signal stage sequence do not align with the actual traffic demands. To address these issues, this paper presents action masking based proximal policy optimization with the dual-ring phase structure (AMPPO-DR), a novel ATSC model based on DRL that can simultaneously optimize the stage sequence and duration. Specifically, we consider the correlation between states and actions and utilize intersection channelization to predict vehicle turning directions. Moreover, we define the action as selecting the next green stage and establish variable-stage-sequence constraint rules on the basis of the dual-ring phase structure. To satisfy the constraints on the stage sequence, we propose the AMPPO algorithm, which dynamically adjusts the policy network outputs to mask invalid stages in real time. The simulation experiments demonstrate that the proposed method can effectively adapt to changing turning flows, enabling flexible and rational stage switching and ultimately increasing traffic efficiency.

Trajectory prediction under Bird’s Eye View (BEV) refers to predicting the movement intention of agents based on the historical observation trajectories, which is of great significance for autonomous driving, driving safety and social navigation. The traffic prediction trajectory is multimodality with multiple reasonable trajectories and various prediction time, which often suffer complex agents interaction, cumulative errors and a large number of agents. To overcome these problems, we explore the BEV-based traffic participants trajectory prediction problem and propose the novel End-point Drive and Reverse Enhanced Decoding Network (EDRED-TPNet), based on the mechanisms of end-point driving and reverse enhanced decoding. Firstly, the Encoder based on Dynamic Spatio-temporal Graph and Multimodality Coding Fusion (ST-MC-Encoder) are constructed to effectively represent complex traffic scenario with changeable agents, encode social interactions with historical trajectories, social interaction, future trajectory and multimodality. Secondly, the End-Point Drive Module is proposed to predict the end point before predicting the complete trajectory, thus providing more accurate trajectory prediction; Lastly, to further improve the long-term prediction performance, the Reverse Enhanced Decoder (RE-Decoder) is proposed to fuse forward and reverse hidden state vectors to obtain diverse trajectories that conform to physical and social acceptability rules. We build the first AAV-captured Trajectory Prediction Dataset (UTP-Dataset) for traffic participants trajectory prediction. Experimental results show that the proposed methods can fulfill the multi-target trajectory prediction task in complex traffic scenarios and achieve high performance.

Autonomous Aerial Vehicles (AAVs) based Vehicle Re-Identification (ReID) brings high flexibility to the ReID system, and also brings challenges of complicated shooting views and special occlusions. In this study, we propose a novel framework for UAV-based vehicle ReID, calledCounterfactual Attention and Joint Promotion based ReID(CAJP-ReID), to deal with fine-grained feature extraction and occlusions. Based on the mechanism of using randomly generated counterfactual attention intervention to train, theCounterfactual Attention Learning Network(CAL-Net) is proposed to learn the fine-grained features of vehicle images, for distinguishing similar vehicles in different ID categories. In order to enhance the diversity of the dataset and the robustness of the network to occluded images, theCounterfactual Attention Enhancement Module(CAE-Module) is proposed based on counterfactual attention mechanism, by the data-enriching mechanism of cropping and erasing. TheHard-sensitive Binomial Joint Promotion Loss(HBJP-Loss) is proposed to comprehensively consider the relative distance and absolute distance of positive and negative samples of vehicle images, and further improves the accuracy of vehicle re-identification. Experiments on two public datasets show that the proposed method achieves State-of-the-Art performance.

This study investigates the problem of road side unit (RSU) location optimization considering vehicle-to-RSU (V2R) communication channel competition. To hedge against the uncertainty of vehicle density, the problem is formulated as a stochastic mixed-integer nonlinear program with equilibrium constraints. This program aims to minimize the expectation of weighted sum of V2R communication delay, packet loss rate and packet collision rate and age of information in V2R communication over all scenarios given RSU location budget limit. Decision variables are RSU locations and the number of connected autonomous vehicles (CAVs) communicating with each located RSU. Equilibrium constraints in the program model V2R communication channel competition among CAVs and ensures the choice of CAVs on RSUs to satisfy user equilibrium principle. The V2R communication is calculated under 6G technology. The program is linearized by using piecewise linearization method. To enhance the solution efficiency, a progressive hedging algorithm is developed to decompose the relaxed linearized model into several subproblems. The optimal solution to the relaxed linearized model is found by iteratively formulating and the solving subproblems. A branch and bound algorithm is introduced to obtain the optimal integer solution to the linearized model. The numerical results show that the proposed model can achieve 20.55% lower total communication delay than the state-of-the-art model only optimizing total V2R information propagation delay, when CAVs choose RSUs for communication in a competitive manner.

Traffic oscillations pose safety and efficiency challenges in mixed scenarios involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Existing control strategies fail to handle the unpredictability of HDV behaviors, resulting in disruptive "stop-and-go" traffic patterns. This study proposes a novel algorithm that uses Deep Reinforcement Learning (DRL) integrated into a distinctive "CAV-AHDV-CAV" structure for car-following events. The consecutive HDVs are treated as an aggregated unit called Aggregated HDVs (AHDVs) to eliminate stochasticity and leverage collective traffic features as inputs, addressing the driver heterogeneity issue. Our training and testing were conducted using a dataset of 9,200 car-following events extracted from the HighD dataset. In these events, the lead vehicle serves as our CAV in front, while the following vehicle represents the AHDV. We simulated our controlled vehicle to follow the AHDV, aiming to achieve the vehicle equilibrium state with respect to both the AHDV and the CAV in front. The results demonstrate a reduction in the impact of HDVs and an enhancement of equilibrium states compared to baseline models. Specifically, we achieved a speed mean square error (MSE) of 3.151 and spacing MSE values of 50.484 (with respect to the AHDV) and 47.855 (with respect to the CAV). These findings offer robust and adaptable control strategies for efficient and safe mixed traffic dominated by CAVs.

This study proposes to find the most appropriate transport modes with an awareness of user preferences (e.g., costs, times) and trip characteristics (e.g., purpose, distance). The work was based on real-life trips obtained from a map application. Several methods including gradient boosting tree, learning to rank, multinomial logit model, automated machine learning, random forest, and shallow neural network have been tried. For some methods, feature selection and over-sampling techniques were also tried. The results show that the best-performing method is a gradient-boosting tree model with the synthetic minority oversampling technique (SMOTE). Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that contains metro, i.e., compared to other modes, people would be more willing to tolerate long-distance metro trips. This indicates that metro lines might be a good candidate for large cities.

Traffic accidents are a significant factor leading to injuries and property losses, prompting extensive research in the field of traffic safety. However, previous studies, whether focused on static environment assessment, dynamic driving analysis, pre-accident prediction, or post-accident rule checks, have often been conducted independently. Our introduces V2X Environmental Perception Multi-modal Large Model AccidentGPT for accident analysis and prevention. AccidentGPT establishes a multi-modal information interaction framework based on multisensory perception. It adopts a holistic approach to address traffic safety issues, providing environmental perception for autonomous vehicles to avoid collisions and maintain control. In human-driven vehicles, it offers proactive safety warnings, blind spot alerts, and driving suggestions through human-machine dialogue. Additionally, it aids traffic police and management agencies in considering factors such as pedestrians, vehicles, roads, and the environment for intelligent real-time analysis of traffic safety. The system also conducts a thorough analysis of accident causes and post-accident liabilities, making it the first large-scale model to integrate comprehensive scene understanding into traffic safety research. Project page: https://accidentgpt.github.io

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.

This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.

Traffic safety, reliability, and resilience are significantly influenced by environmental factors such as visibility, road surface, and weather conditions. Yet, current monitoring methods, including weather stations and onboard environmental sensors, often fall short due to their high costs, significant latency, and limited dissemination. This paper presents the Edge-based Multi-task Safety-oriented Environmental (Edge-MuSE) sensing system, designed to address these traffic safety challenges associated with environmental factors. Edge-MuSE departs from traditional single-task sensing methods by performing multidimensional traffic environment perception tasks. It estimates key safety-related environmental factors exclusively through camera inputs and incorporates four innovative sensing tasks: visibility estimation, image dehazing, road segmentation, and road surface condition classification. The system is tailored to edge devices to transition computational loads from central servers to distributed nodes, thereby enhancing privacy and reducing latency. Additionally, Edge-MuSE integrates communication functions based on TCP/IP and Wi-Fi protocols, enabling rapid dissemination of sensing results and warning messages to local road users. System structures and data streaming have been optimized to accommodate the constraints of edge devices, ensuring high-efficiency edge computing. Field testing of Edge-MuSE in multiple testbeds in Bellevue (WA, US) and Oslo (Norway) has demonstrated its reliable and precise performance in perception tasks (92.15% accuracy in visibility estimation and 92.25% in road surface condition classification) as well as an impressive processing speed of 21.3 FPS. As such, Edge-MuSE presents a promising solution for enhancing roadway safety, efficiency, and resilience.

Describing a traffic scenario from the driver’s perspective is a challenging process for Advanced Driving Assistance System (ADAS), involving different sub-tasks of detection, tracking, segmentation, etc. Previous methods mainly focus on independent sub-tasks and have difficulties to comprehensively describe the incidents. In this study, this problem is novelly treated as a video captioning task, and a Guidance Attention Captioning Network (GAC-Network) structure is proposed for describing the incidents in a concise single sentence. In GAC-Network, an Attention based Encoder-Decoder Net (AED-Net) is built as the main network; with the temporal spatial attention mechanisms, the AED-Net make it possible to effectively reject the unimportant traffic behaviors and redundant backgrounds. Considering various driving scenarios, the Spatio-Temporal Layer Normalization is used to improve the generalization ability. To generate captions for incidents in driving, the novel Guidance Module is proposed to boost the encoder-decoder model to generate words in a caption, which have better relationship to the past and future words. Because there is no public dataset for captioning of driving scenarios, the Traffic Video Captioning (TVC) dataset is released for the video captioning task in driving scenarios. Experimental results show that the proposed methods can fulfill the captioning task for complex driving scenarios, and achieve higher performance than the methods for comparison, including at least 2.5%, 1.8%, 3.6%, and 13.1% better results on BLEU_1, METEOR, ROUGE_L and CIDEr, respectively.

This paper proposes a methodology combining Long-Short-Term-Memory (LSTM)-assisted kinematic motion prediction with a hybrid coding algorithm for compressing the trajectory data of Connected Autonomous Vehicles (CAVs). The vehicle locations after the first two time steps are predicted based on the vehicle positions at the first two time steps and the kinematic equation. The vehicle velocities and accelerations are predicted based on the vehicle locations and LSTM. The hybrid coding algorithm integrates differential coding, Binary Coded Decimal (BCD) coding and arithmetic coding. Differential coding converts the original data into the difference between the original data and the predicted data. Since the length of the original data is large but the difference between it and predicted data is small, the required space for storing the data can be greatly reduced. BCD coding converts subsequences of different lengths to the subsequences with the same length so that the original information can be correctly reproduced after decompression. Arithmetic coding expresses the information in small space by converting the character sequence into a decimal between 0 and 1. The proposed algorithm is evaluated on the Next Generation Simulation Trajectory dataset. The experiment results show that the compression ratio and compression rate obtained by the proposed algorithm are respectively higher and lower than those obtained by the baseline algorithms. Also, the sum of compression time, decompression time and transmission time associated with the proposed algorithm is less than that associated with most baseline algorithms and transmission without compression.

Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment. In the meantime, evidence shows that social media data provides valuable clues about physical and mental health conditions. In this paper, we argue that it is feasible to identify depression at an early stage by mining online social behaviours. Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection. Instead, we propose a novel classifier, namely, Cost-sensitive Boosting Pruning Trees (CBPT), which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets. To comprehensively evaluate the classification capability of CBPT, we use additional three datasets from the UCI machine learning repository and CBPT obtains appealing classification results against several state of the arts boosting algorithms. Finally, we comprehensively explore the influence factors for the model prediction, and the results manifest that our proposed framework is promising for identifying Twitter users with depression.

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.

Managing and estimating the availability information of parking lots is of great importance to travelers and managers. However, the task is very challenging since the occupancy rate is affected by various factors, including spatial-temporal features, parking lot attributes features, and environmental changes. Previous studies mostly focus on the short-term prediction by capturing the historical sequential dependencies among inputs and outputs, which leads to low estimation accuracy for long-term prediction and limited scalability for real deployment in parking lots. To address the challenges, a comprehensive framework for real-time Smart Parking Data Management and Prediction (SPDMP) system is proposed. Three types of data sources, including historical sequential data, real-time sequential data, and attributes category data, are sufficiently integrated into a customized Parking Availability Prediction (PAP) neural network by representation learning and heterogeneous feature embedding. Specifically, instead of using parking lot property information and environmental data directly, the authors design an Attributes Tensor Embedding Component (ATEC) to integrate the intra-class affection and interclass representations and correlations by two steps of customized embedding process. To balance the impact of the indiscriminate features for various prediction targets, this study proposes a multi-factor attention mechanism to learn the weights and help the PAP achieve a stable performance for time-various tasks. From extensive experiments on two real-world large-scale data sets collected in China and USA (including 19 urban parking and 2 truck parking lots), PAP achieves superior prediction results on both urban parking (6.69% average MAPE) and truck parking prediction (7.63% average MAPE) for five prediction time slots (5 min, 10 min, 30 min, 1 h, and 2 h). Furthermore, even with limited training data, PAP still shows better transferability and adaptability for various types of lots. The proposed SPDMP is selected and used as a pilot test bed by the Washington State Department of Transportation (WSDOT) for truck parking information promotion.

Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors’ detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy$C$-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network.

Accurate destination prediction over sub-trajectories is essential for a wide range of location-based services. Traditional trip matching methods fail to capture temporal dependence hidden in trajectories and may suffer from data sparsity problems. With the help of massive trajectory data, state-of-the-art approaches based on deep learning (DL) have achieved great success. However, existing DL approaches rarely consider the influence of individual travel preferences in destination prediction. When the trip is long but the known partial trajectory is short, DL models are unable to produce satisfactory results. Thus, we design a feature extraction mechanism to extract useful temporal features, spatial features, and static covariates for destination prediction, among which the spatial features characterize individual travel preferences by considering two main movement patterns in daily travel. Then, a hierarchical model including multiple modules is proposed to finely process heterogeneous features. Extensive experiments conducted on two public datasets demonstrate the superior performance of the proposed model compared to the state-of-the-art methods. Moreover, further experimental results show that the proposed model still performs well when trajectory prefix is short or travel duration is long, which confirms the effectiveness of integrating individual travel preferences.

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient$\varepsilon $-constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework.

Machine vision based vehicle re-identification (ReID) plays an important role in some Intelligent Transportation Systems (ITS). Yet, most previous methods mainly focus on fixed surveillance cameras instead of Unmanned Aerial Vehicle (UAV). With high flexibility, the UAV-based vehicle ReID problem has some special challenges including complicated shooting angles, low discrimination of top-down features, and large variance in vehicle scales, etc. To overcome these challenges, we propose a novel structure for UAV-based vehicle ReID without license plates. Firstly, a triple-head segmentation net is proposed for segmenting UAV-captured vehicles under different heights and directions. Secondly, a posture calibration model is designed to uniform the vehicle postures based on the segmentation results, with the purpose of reducing the influence of different postures. Thirdly, the novel Cross-View & Hard-Sensitive Metric Learning (CHSML) method is proposed to train a ReID network with cross-view training constraint and hard sensitive principles; the mechanism of CHSML takes the cross-view samples of same ID as a training unit to learn the potential visual relationship in cross-view and builds a hard sensitive weight matrix to make learning more focus on hard samples, which improves the low ReID accuracy brought by cross-view or hard samples. Moreover, to facilitate the research of UAV-based vehicle ReID, a large-scale UAV-based vehicle ReID dataset called VeRi-UAV is released with 17 516 vehicles of 453 IDs. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task.

Traffic congestion is a long-lasting worldwide problem and even becoming more severe in well-developed regions. Reversible lanes have been used worldwide on various road types to mitigate the effects of congestion and optimize mobility since the 1930s. However, with the limitation of traditional control and management methods, existing solutions can not meet the increasing travel demands. Therefore, the paper introduces a Predictive Empowered Assignment scheme for Reversible Lane (PEARL). By integrating the advanced traffic flow prediction module and bi-level optimization model, PEARL can be a more flexible dynamic lane assignment strategy with foresight compared to traditional lane management methods. In the prediction module, taking advantage of the development of machine learning technologies, the input of PEARL covers not only historical sequence and real-time data but also the environmental conditions in the region. The advanced Bi-directional Long Short-term Memory (Abi-LSTM) model is employed for short-term traffic flow prediction. Then, the study introduces a bi-level optimization method to maximize the total throughput in both directions and minimize the total user costs which determine the lane deployment. The iterations on the prediction module and optimization module can help PEARL coordinate the future lane control plan and make the best decision. Finally, the paper builds up an experiment platform to simulate PEARL in a real-world scenario with heavy input flow for its performance evaluation.

Traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.

Surveillance cameras are widely deployed traffic sensors, due to their affordable prices and being able to capture rich information. However, current surveillance systems have not been fully exploited: these cameras are isolated and can only extract information from their own fixed views. To enable a collaborative sensing system, we propose a novel framework called Traffic-Informed Multi-camera Sensing (TIMS) system for network-level traffic information estimation. By pushing multi-camera Re-IDentification (ReID) workflow towards network-wide traffic information extraction, TIMS system integrates a customized metric-learning vision-based vehicle ReID method (TIM-ReID) and establishing a traffic-informed workflow. To integrate the traffic network connection information along with visual and vehicle attributes features, the road network is extracted as a weighted graph through the Spatial-temporal Camera Graph Inference Model (StCGIM) and serves for matching and re-ranking ReID candidates. Moreover, an Accuracy Model (AAM) is designed to provide accurate, reliable and comprehensive traffic information estimation, including both the values and distribution of parameters under a high penetration rate. In experiments based on real-world multi-camera datasets captured in the city of Seattle, the customized TIM-ReID outperforms existing state-of-the-art methods, and delivers accurate cross-camera information estimation, whose value error is less than 8% and the Kullback-Leibler (KL) distance between the estimated and real distribution is less than 3.42 among all the evaluated camera pairs. TIMS system empowers cameras to work collaboratively through an interactive brain, and provides users with valuable and comprehensive traffic information.

Customized path-based speed prediction is an eventful tool for congestion avoidance, route optimization and travel time prediction for navigation apps, cab-hailing companies and autonomous vehicles. Traditionally, the speed prediction algorithms are based on road segments and can only support several main roads. Path-based speed prediction is very challenging since the speed is always changing in different path locations and is jointly affected by lots of complicated factors. This article presents a novel deep learning framework for customized path-based speed prediction. A Path-based Speed Prediction Neural Network (PSPNN) is designed to achieve speed predictions for a given path and attributes information. A hierarchical Convolutional Neural Network (CNN) and deep Bidirectional Long Short-Term Memory (Bi-LSTM) structure for different kinds of feature extraction are applied for multiple levels: the path cell, sub-path and the whole path. The method narrows down the prediction unit from road segments to customized path cells (mean length: 59.52m) and achieves a mean absolute error (MAE) of 1.94 m/s and Mean Absolute Percentage Error (MAPE) of 18.14%, showing the potential of serving rigorous data-driven applications. So far, PSPNN is the first made-to-order path-based speed prediction algorithm and can help both travelers and managers to obtain large-scale bespoke paths speed information in advance.

With the significant increase of e-commerce, freight transportation demand has surged significantly over the past decade. Most of the demand has been served by trucks in the United States. One major problem commonly identified across the country is the worsening truck parking availability because the increase of truck parking facilities has lagged behind the growth of trucking activities. The lack of parking spaces and real-time parking availability information greatly exacerbate the uncertainty of trips, and often results in illegal and potentially dangerous parking or overtime driving. This paper elaborates on pilot research on improving truck parking facilities cooperated with the Washington State Department of Transportation (WSDOT), building and testing the advanced Truck Parking Information and Management System (TPIMS) with the real-time user visualization and prediction function empowered by artificial intelligence. Furthermore, by analyzing the activities of truck drivers, the researchers aggregated the regularity of truck parking patterns by a customized sequential similarity methodology. A Truck Parking Occupancy Prediction (TPOP) neural network for time-variant occupancy prediction by deep learning and attributes embedding is proposed and integrated into the TPIMS. The TPOP achieves 5.82%, 5.07%, 4.84%, and 4.19% mean average percentage error (MAPE) for 16, 8, 4, and 2 minutes ahead of occupancy prediction respectively, significantly outperforms other state-of-the-art methods. Clearly, the proposed solutions can benefit both the truck drivers and government agencies by a more efficient and smart TPIMS.

Travel data is vital for understanding individual travel behaviors and estimating travel demand. Compared with traditional travel surveys in which respondents were asked to recall their trips, smartphones can record GPS trajectories actively at a high level of accuracy in both space and time. However, how to identify trips and detect travel modes from raw GPS data in an efficient way is still challenging. To address these issues, we firstly target high-accuracy data collection through developing a smartphone-based person travel survey system which comprises of three components, i.e., data collector, data processor, and data validator. The data collector is a smartphone app for collecting GPS trajectories; the data processor is a server embedded with rule-based algorithms to extract travel-activity information; and the data validator is a webpage to present the self-extracted travel information for respondents’ validation. Secondly, we propose a hybrid random forest and merging algorithm to detect multiple travel modes in complex trips. The algorithm reaches an accuracy of 100% for simple trips and 95.7% for multimodal trips. These results indicate that the proposed hybrid random forest and merging algorithm can help identify travel modes of complex trips more accurately and efficiently.

Real-time public transit ridership flow and origin-destination (O-D) information is essential for improving transit service quality and optimizing transit networks in smart cities. The effectiveness and accuracy of the traditional survey-based methods and smart card data-driven methods for O-D information inference have multiple disadvantages in terms of biased results, high latency, insufficient sample size, and the high cost of time and energy. By considering the ubiquity of smart mobile devices in the world, monitoring public transit ridership flow can be accomplished by passively sensing Wi-Fi and Bluetooth (BT) mobile devices of passengers. This study proposed a system for monitoring real-time public transit passenger ridership flow and O-D information based on customized Wi-Fi and BT sensing device. By combining the consideration of the assumed overlapping feature spaces of passenger and nonpassenger media access control address data, a three-step data-driven algorithm framework for estimating transit ridership flow and O-D information is proposed. The observed ridership flow is used as the ground truth for evaluating the performance of the proposed algorithm. According to the evaluation results, the proposed algorithm outperformed all selected baseline models and the existing filtering methods. The findings of this study can help to provide real time and precise transit ridership flow and O-D information for supporting transit vehicle management and the quality of service enhancement.

The customized bus (CB) is an innovative type of transit service that can provide a personalized efficient transit services for passengers and environmental friendliness and congestion alleviation in metropolitan areas. This work develops an integrated optimization method for CB stop deployment, route design, and timetable development optimization problems while meeting travel demands as much as possible to obtain system-optimal CB service plans. Through the perspective of space-time network, the CB service design problem (CBSDP) is formulated as an integrated optimization model with the objectives of maximizing passenger accessibility and minimizing operating cost. An inconvenience index of passengers is introduced in the problem to measure the service quality, and the total number of stops for all involved CB routes is set as one of the objectives to optimize the total cost of the CB system. A heuristic approach is applied to generate efficient solutions for the CBSDP. Two types of instance, namely, a numerical experiment and a real-world instance, are implemented to demonstrate the performance of the proposed method. We also conduct a series of sensitive analyses to explore the influences of various parameters on the CB system for capturing the interaction among stops, routes, and timetables. Final results show that the CB plans obtained by the proposed method can provide efficient services by balancing passenger convenience and operating cost.

Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing all or part of the data locally at the edge of a surveillance system. In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed either locally at the edge of the system or on the centralized data server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent transportation systems.

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.

Variable speed limits (VSL) is a major Intelligent Transportation System (ITS) technology for controlling freeway mainline traffic, which have been increasingly used to improve traffic safety and operations efficiency of freeway traffic management. The primary objective of this study is to evaluate the safety impacts of the VSL system implemented on Interstate 5 in Seattle, United States since 2010. A Full Bayesian (FB) before-after analysis was conducted based on 9,787 crashes that occurred in a 72-month study period. The analysis was conducted for all crashes, crash severity levels, crash types, and crash causes. The FB before-after results implied that the total crash count was reduced by 32.23% with a standard deviation of 3.58% after the VSL system was applied on the freeway. The count of crashes with no injury decreased more than crashes with severe injury and possible injury. The effect of rear-end crash reduction was the most beneficial among all crash types, while the effect on sideswipe crash reduction was the least. The study also compared the traffic speed features in the before and after periods to fully evaluate the impacts of the VSL system on traffic operations. The result indicated that, with VSL control, the difference in speed was reduced by the VSL system. The results of this study are particularly valuable for policy and control strategy development, and cost-benefit evaluation associated with VSL system implementations.

Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.

The rapid development of traffic theory and information technology has provided diversified and large-scale traffic data resources for traffic research and urban traffic management. At the same time, these data also present many challenges, such as missing data and deviations in data collection. Many researchers have reported that inaccurate or incomplete measurements of traffic variables can be corrected based on either traditional traffic flow theory, which ignores the randomness of traffic, or are performed using machine learning methods, which emphasize data quantity, but do not make effective use of domain knowledge. This paper proposes a Traffic Factor State Network framework defined by traffic factors and their links to represent the relationships between traffic factors; this framework includes not only obvious traffic factors like volume and speed, but also hidden traffic factors such as the environmental impact factor, which is a variable used to represent complex road conditions. This variable is used to describe the influence of non-traffic flow parameters such as road condition and environmental factors, and is estimated by the EM (Expectation Maximization) algorithm based on historical data. This study used a high-order multivariate Markov model to implement the TFSN, which was then used to establish a stochastic model of speed and related factors. A large amount of historical data was used to calculate and calibrate the strength of the links between the model factors. Finally, a stochastic model of speed prediction was established. The verification results compared with actual cases demonstrate the validity and applicability of the proposed model.

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model's loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

This paper introduces a model to analyze route choice behavior of taxi drivers for finding next passenger in urban road network. Considering the situation of path overlapping between selected routes in the process of customer-searching, a mixed path size logit (MPSL) model is proposed to analyze route choice behaviors through considering spatio-temporal features of route including customer generation rate, path travel time, cumulative intersection delay, path distance, and path size. Specially, customer generation rate is defined as attraction strength based on historical pick-up records in the route, the intersection travel delay and path travel time are estimated based on large scaled taxi global positioning system (GPS) trajectories. In the experiment, the GPS data were collected from about 36000 taxi vehicles in Beijing at 30-s interval during six months. In the model application, an area of approximately 10 square kilometers in the center of Beijing is selected to demonstrate the effectiveness of the proposed model. The results indicated that the MPSL model could effectively analyze the route choice behavior in customer-searching process and express higher accuracy than traditional multinomial logit model and basic PSL model.

Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.

Unmanned aerial vehicles (UAVs) are gaining popularity in traffic monitoring due to their low cost, high flexibility, and wide view range. Traffic flow parameters such as speed, density, and volume extracted from UAV-based traffic videos are critical for traffic state estimation and traffic control and have recently received much attention from researchers. However, different from stationary surveillance videos, the camera platforms move with UAVs, and the background motion in aerial videos makes it very challenging to process for data extraction. To address this problem, a novel framework for real-time traffic flow parameter estimation from aerial videos is proposed. The proposed system identifies the directions of traffic streams and extracts traffic flow parameters of each traffic stream separately. Our method incorporates four steps that make use of the Kanade-Lucas-Tomasi (KLT) tracker, k-means clustering, connected graphs, and traffic flow theory. The KLT tracker and k-means clustering are used for interest-point-based motion analysis; then, four constraints are proposed to further determine the connectivity of interest points belonging to one traffic stream cluster. Finally, the average speed of a traffic stream as well as density and volume can be estimated using outputs from previous steps and reference markings. Our method was tested on five videos taken in very different scenarios. The experimental results show that in our case studies, the proposed method achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively. The method also achieves a fast processing speed that enables real-time traffic information estimation.

This paper proposes a new method in construction fuzzy neural network to forecast travel speed for multi-step ahead based on 2-min travel speed data collected from three remote traffic microwave sensors located on a southbound segment of a fourth ring road in Beijing City. The first-order Takagi-Sugeno system is used to complete the fuzzy inference. To train the evolving fuzzy neural network (EFNN), two learning processes are proposed. First, a K-means method is employed to partition input samples into different clusters and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated. Second, a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Furthermore, a trigonometric regression function is introduced to capture the periodic component in the raw speed data. Specifically, the predicted performance between the proposed model and six traditional models are compared, which are artificial neural network, support vector machine, autoregressive integrated moving average model, and vector autoregressive model. The results suggest that the prediction performances of EFNN are better than those of traditional models due to their strong learning ability. As the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.

This paper proposes a two-layer decision framework to model taxi drivers' customer-search behaviors within urban areas. The first layer models taxi drivers' pickup location choice decisions, and a Huff model is used to describe the attractiveness of pickup locations. Then, a path size logit (PSL) model is used in the second layer to analyze route choice behaviors considering information such as path size, path distance, travel time, and intersection delay. Global Positioning System data are collected from more than 36 000 taxis in Beijing, China, at the interval of 30 s during six months. The Xidan district with a large shopping center is selected to validate the proposed model. Path travel time is estimated based on probe taxi vehicles on the network. The validation results show that the proposed Huff model achieved high accuracy to estimate drivers' pickup location choices. The PSL outperforms traditional multinomial logit in modeling drivers' route choice behaviors. The findings of this paper can help understand taxi drivers' customer searching decisions and provide strategies to improve the system services.

In this paper, a self-adaptive tolling strategy (SATS) is developed for dynamically and systematically enhancing highoccupancy toll (HOT) lane system operations. This strategy enhances the overall system performance of both the HOT and general purpose (GP) lanes by better utilizing the HOT lane capacity while maintaining high speed and/or high travel-time reliability for HOT lane traffic when GP lanes are congested. To formulate SATS, the Lighthill-Whitham-Richards kinematic wave model is used to characterize HOT lane traffic flow evolution, and the unilateral Laplace transform is used to convert the system representation from the time domain to the frequency domain. Then, an adaptive tolling controller is designed with both the proportional and integral control components. Real-time traffic measurements, including lane occupancy, average speed, and flow rate, are utilized for toll rate calculations. Following a dual-phase control scheme, the appropriate flow rate for HOT lane utilization is computed, and the corresponding toll is estimated backward. To examine the effectiveness of the proposed tolling strategy, microscopic traffic simulation experiments are conducted using VISSIM. The experiment results demonstrate that the proposed tolling strategy performs reasonably well in improving the overall operations of HOT lane systems under various traffic conditions.

The automated fare collection (AFC) system, also known as the transit smart card (SC) system, has gained more and more popularity among transit agencies worldwide. Compared with the conventional manual fare collection system, an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival. Although it is possible to collect highly valuable data from transit SC transactions, substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection. This is true especially for the Beijing AFC system, where a passenger’s boarding stop (origin) on a flat-rate bus is not recorded on the check-in scan. To extract passengers’ origin data from recorded SC transaction information, a Markov chain based Bayesian decision tree algorithm is developed in this study. Using the time invariance property of the Markov chain, the algorithm is further optimized and simplified to have a linear computational complexity. This algorithm is verified with transit vehicles equipped with global positioning system (GPS) data loggers. Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy. Such transit origin data are highly valuable for transit system planning and route optimization.

Optimization of signal control at isolated intersections has been an important research focus in traffic engineering over the past few years. Due to its flexibility and practicality, fully actuated control has been extensively deployed. In the conventional actuated control scheme, two important parameters, i.e., minimum and maximum green times, are arbitrarily prespecified, although it is widely recognized that they can significantly impact system operations. Previous studies have concentrated on computing these parameters using deterministic models. Due to the stochastic features of traffic arrival, such statically designated green time boundaries cannot sufficiently handle various traffic demands. To solve this problem, a stochastic model is established to dynamically optimize the minimum and maximum green times using real-time queue lengths and traffic arrival characteristics for each phase. Multiple criteria are fused and exploited as control objectives, such as avoiding cycle failures, minimizing control delays, and maximizing total traffic throughputs. Performance of the proposed algorithms is examined using a microscopic traffic simulation program, i.e., VISSIM 4.30, under various scenarios. The results show that the control system operated by the proposed algorithm produces promising improvements in system operation efficiency and fairness under various traffic demands.