Trajectory prediction for individual vehicles has emerged as a vital component in Internet of Things (IoT)-based traffic management applications, inducing various control strategies for alleviating traffic congestion. This study focuses on a novel topic in this field, i.e., making joint predictions for the next location and travel time. Based on principles of vehicle mobility, we learn vehicle trajectories as discrete events in the spatiotemporal dimension and propose a neural temporal point process, named TrajTPP. This model employs two attention mechanisms to learn spatial and temporal dependencies, respectively, and a novel recurrent structure is proposed to integrate spatiotemporal features. Meanwhile, a gated residual attentive network (GRAN) is also designed to combine these learned dynamic features with static travel information. Then, the intensity-free learning strategy is employed to make probabilistic forecasting for the next travel times, and we develop a prior transition probability to involve historical travel behaviors in location predictions. Beyond the conventional prediction task, we design a sampling strategy to simulate vehicle mobilities by TrajTPP. Experiments from license plate recognition data in Changsha, China, demonstrate that our model outperforms advanced baselines, and sampling results provide evidence of its ability to accurately simulate vehicle mobilities. Moreover, its impressive accuracy on the latest next-location prediction benchmark is also listed in the Appendix.

This study investigates the problem of joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling. The problem is formulated as a bi-level mixed-integer bilinear program to capture passengers’ realistic path choice behavior. The upper-level model aims to minimize the weighted sum of the cost of bus route construction, bus route operation, bus station construction, travel time of passengers, the delay caused by failures in aboarding to the bus trips at the origin, the delay caused by failures in transfer between the bus trips, and the overflow delay when the bus trip operates at capacity. The lower-level model aims to minimize the travel time of passengers. The travel time of passengers is formulated as the sum of the waiting time for boarding, the transfer time, and the in-vehicle travel time. The passenger transfer time and the delay caused by failures in transfer between the bus trips are formulated with exact modeling of passenger transfer behavior. This bi-level mixed-integer bilinear program is transformed into an equivalent mixed-integer bilinear program with equilibrium constraints using Karush-Kuhn-Tucker conditions. To seek a solution of good quality to the proposed model while not requiring a large amount of computer memory, a Benders decomposition algorithm integrated with piecewise linearization is developed. A numerical application demonstrates that the proposed model is able to achieve 3.49% lower total cost than the baseline model assuming passenger transfer time to be half of the headway.

The dedicated lanes management policy is a possible solution to the issues arising from the coexistence of human-driving vehicles (HDVs) and connected and autonomous vehicles (CAVs) in traffic. Although numerous studies have been conducted on the network deployment problem of CAV-dedicated lanes, the safety implications of CAV and its dedicated lanes are ignored. This study proposes a mathematical approach to optimize the deployment of CAV-dedicated lanes incorporating efficiency and safety concerns. An integrated framework is developed based on headway distributions to systematically evaluate the efficiency and safety performance of the road network. The platoon intensity index is utilized to model the platooning effect of CAVs on traffic safety and efficiency. A safety performance estimation method is proposed to account for the potential collision risk of mixed traffic flow and heterogeneity in car-following behavior. A bi-level programming model is adopted to solve the optimal deployment problem. The upper-level model is formulated as a bi-objective model to minimize the total travel time and the safety risk. The lower-level model describes the multi-class user equilibrium state of the CAV-HDV mixed traffic flow on the network. A genetic algorithm is utilized to solve the bi-level programming model and obtain the Pareto-optimal solution set. Two numerical studies are conducted to validate the proposed model and algorithm. The results revealed that the optimal deployment plan can significantly improve the road network’s safety and efficiency performance, whereas higher platoon intensities have a negative impact on traffic safety and efficiency for certain headway settings. Moreover, the results highlighted a trade-off between efficiency and safety in the optimal deployment problem, which may help decision-makers choose the optimal deployment plan based on the road network design needs.

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.

The COVID-19 pandemic has caused a dramatic change in the demand composition of restaurants and, at the same time, catalyzed on-demand food delivery (OFD) services—such as DoorDash, Grubhub, and Uber Eats—to a large extent. With massive amounts of data on customers, drivers, and merchants, OFD platforms can achieve higher efficiency with better strategic and operational decisions; these include dynamic pricing, order bundling and dispatching, and driver relocation. Some of these decisions, and especially proactive decisions in real time, rely on accurate and reliable short-term predictions of demand ranges or distributions. In this paper, we develop a Poisson-based distribution prediction (PDP) framework equipped with a double-hurdle mechanism to forecast the range and distribution of potential customer demand. Specifically, a multi-objective function is designed to learn the likelihood of zero demand and approximate true demand and label distribution. An uncertainty-based multi-task learning technique is further employed to dynamically assign weights to different objective functions. The proposed model, evaluated by numerical experiments based on a real-world dataset collected from an OFD platform in Singapore, is shown to outperform several benchmarks by achieving more reliable demand range forecasting.

Emerging applications in real-time traffic management put forward urgent requirements for lane-level traffic flow prediction. Limited by extremely unstable traffic volumes and heterogeneous spatiotemporal dependencies in urban road networks, network-scale prediction for lane-level traffic flow is still a critical challenge. This study models the dynamic characteristics of lane-level traffic flow as complex networks and proposes a deep learning framework for network-scale prediction. Relying on the visibility graph, we transform the temporal dependence learning task into spatial correlation mining on temporal complex networks. For spatial dependency extraction in urban traffic flows, we establish three topological graphs from traffic, statistical, and semantic perspectives to investigate the static and dynamic correlations. Then, a network-scale traffic volumes prediction model, i.e., spatiotemporal multigraph gated network (STMGG), is proposed to learn spatiotemporal correlations on visibility graphs and spatial topological graphs. This model designs an attention-based gated mechanism to incorporate global features from multigraphs. Additionally, a Seq2Seq structure is integrated to enhance multistep prediction stability. We employ two license plate recognition (LPR) datasets as case studies, and STMGG expresses superiorities over various advanced deep learning models. Meanwhile, an ablation experiment is conducted to evaluate its components, and numerical tests further reveal its impressive inductive learning capability.

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.

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January$1^{\mathrm{st}}$, 2020, to February$28^{\mathrm{th}}$, 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels.

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.

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.

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.

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.