Estimating trip travel time is indispensable in intelligent transportation systems, especially for the mobile navigation Apps and ride-hailing services. However, the accurate travel time estimation (TTE) is a non-trivial task due to the challenges of time-varying traffic conditions, complex road networks, and external influences. Learning to estimate the travel time from trajectories is prevalent in emerging studies. The explosion of trajectory data facilitates the implementation of end-to-end deep learning models, making them a powerful tool for the TTE. Whereas, the inherent geographic contexts of trajectories are neglected in previous efforts. The inadequate information usage and incomplete feature representation would inevitably result in somewhat unsatisfactory performances and the weak generalization ability. To tackle such problems, we propose a novel deep representation learning approach. Firstly, GPS points of trajectories are encoded via geographic grids, which eases the modeling of spatiotemporal features. Secondly, the learnable matrices of geographic grids and traveled distance undergo a multi-head self-attention module with the positional encoding to capture their inner dependencies. And then, the external influential features of the departure time, day-of-week, and weather information are incorporated into the model through the embedding manipulation. Finally, across-attention module is leveraged to merge all representation results and gain the desired output. Validated on two real-world datasets from Beijing and Chengdu, China, our method significantly outperforms state-of-the-art baselines by 4%-11%. The implementation code of our model is available at the repository:https://github.com/hang-xu-suda/ATRLNN

Transformer-based methods have achieved remarkable performance in event-based object detection, owing to the global modeling ability. However, they neglect the influence of non-event and noisy regions and process them uniformly, leading to high computational overhead. To mitigate computation cost, some researchers propose window attention based sparsification strategies to discard unimportant regions, which sacrifices the global modeling ability and results in suboptimal performance. To achieve better trade-off between accuracy and efficiency, we propose Sparse Mamba (SMamba), which performs adaptive sparsification to reduce computational effort while maintaining global modeling capability. Specifically, a Spatio-Temporal Continuity Assessment module is proposed to measure the information content of tokens and discard uninformative ones by leveraging the spatiotemporal distribution differences between activity and noise events. Based on the assessment results, an Information-Prioritized Local Scan strategy is designed to shorten the scan distance between high-information tokens, facilitating interactions among them in the spatial dimension. Furthermore, to extend the global interaction from 2D space to 3D representations, a Global Channel Interaction module is proposed to aggregate channel information from a global spatial perspective. Results on three datasets (Gen1, 1Mpx, and eTram) demonstrate that our model outperforms other methods in both performance and efficiency.

Demand Responsive Transport (DRT) plays a crucial role in mitigating the inefficiencies of current public transit systems. Efficient routing is paramount for enhancing the flexibility and applicability of this transportation mode. Machine learning techniques, such as the attention-based encoder-decoder methodology, have the capability to produce solutions within seconds after offline training. However, these algorithms encounter convergence issues during training process, and demonstrate limited generalization ability, particularly across different scales. Thus, this paper proposes a graph prompt learning-based method comprising an information encoder, token generation, and token mapping to effectively train models that can adapt to diverse vehicles and demand variations. Particularly, token generation considers the characteristics of the problem by integrating vehicle and customer urgency information each time step. Token mapping obtains vehicle decoding sequences through attention mechanisms and mask function. The proposed model's performance is comprehensively evaluated against commonly baselines across various request contexts. Results show that our method can significantly reduce the computational time, and improve the quality of routing solution compared with baselines. Overall, the proposed model can enhance the routing efficiency of DRT systems through token mapping and prompts design.

Accurate and robust short-term traffic prediction is critical for the advancement of Intelligent Transportation Systems (ITS). With the accessibility of more fine-grained traffic data, intersection queue lengths prediction with both high spatial and temporal resolution have become feasible, offering significant potential for applications such as dynamic and adaptive traffic management in real-time systems. However, predicting these fine-grained queue lengths is challenging due to their rapid fluctuations and the zero-inflated, long-tailed nature of the data. To address these challenges, this paper proposes CTSG-Net (Cyclic Temporal-Spatial Graph Convolution Network), capable of predicting lane-level, seconds-by-seconds queue lengths at a network scale. CTSG-Net incorporates a cyclic feature extraction module that utilizes signal cycle information to capture periodic traffic patterns, and a graph topology learning module that combines static and dynamic graph layers to model lane-specific spatial dependencies. Additionally, CTSG-Net employs a weighted loss function to further address the zero-inflated nature of queue length data. Experiments have been conducted on real-world traffic data from Yizhuang, Beijing, during both off-peak and evening peak periods. The results demonstrate that the proposed CTSG-Net consistently achieves state-of-the-art performance. Specifically, for off-peak and evening peak scenarios, the proposed CTSG-Net demonstrates substantial improvements of up to 8.04%, 28.23%, 24.68% in MAE and 6.54%, 27.75%, 22.12% in RMSE across dataset of all queue lengths, non-zero queue lengths, and large queue lengths ($\ge 100$m), respectively. The source code is available at:https://github.com/cherryh2021/CTSG-Net.git

Bus platooning on urban bus corridors can improve the efficiency of dedicated bus lanes and reduce energy consumption. However, due to the multiple routes operating on the bus corridor, bus platooning may induce congestion at the bus stops and disturb regular headway. To address these bus operational issues, this paper proposes a two-stage dynamic bus platooning model considering bus routes and provides safe and efficient trajectory planning for buses in each platoon. To solve the proposed model, a Particle Swarm Optimization embedded Dynamic Programming algorithm is customized to find the time of leaving and joining the platoon, and Broyden-Fletcher-Goldfarb-Shanno Sequential Quadratic Programming method is used to control the trajectories of bus platooning in real time. Using the SUMO simulator, a real-world case study is conducted to show the effectiveness of the proposed method over traditional methods. The results show that the fuel consumption is reduced by 18.23%, operational efficiency is improved by 8.15%, and the average passenger waiting time is decreased by 5.73%. Sensitivity analysis reveals the impact of the number of routes, fleet size and stop spacing on system performance. The proposed dynamic platooning method outperforms the CACC method across different fleet sizes. Results show that the desired fleet size is greater than 60 buses, the stop spacing is greater than 500 m, especially in large cities or long-distance bus systems.

An increasing number of vehicles cause the deteriorating congestion problem, which leads to the excessive time spent in commuting. Thus, finding fast driving paths gathers growing interest from travelers and governments. However, effective route planning in traffic networks is deemed to be a considerable challenge due to the complex variations of traffic conditions. The existing traffic state aware routing strategies include two main categories: trip planning based on the traffic state before departure or short-term traffic prediction. The former may incur newly emerged jams because of the rapid en-route evolution of traffic state. The nonnegligible prediction errors would make the latter method deviate from the optimal paths. Moreover, these two deterministic routing strategies may easily result in late arrival for important events. To address this nontrivial problem, we propose a sophisticated route planning approach. Specifically, our method employs empirical observations of traffic prediction results to develop statistical models of error distributions. Then, the empirical error distributions are incorporated with the speed prediction values to constitute the stochastic and time-varying (STV) road network model. Thirdly, the least expected time (LET) routing problem in this STV network is defined. To determine the LET paths, we develop the time-varying K-fastest paths algorithm to generate the candidate paths and the discrete numerical method to compare their expected travel time. Finally, we collect the real-world traffic speed and trajectory data for experiments. The comparison results validate that our approach achieves the best performance and the improvement over other baselines is significant in peak hours.

With the advent of autonomous driving technologies, traffic control at intersections is expected to experience revolutionary changes. Various novel intersection control methods have been proposed in the existing literature, and they can be roughly divided into two categories: vehicle-based traffic control and movement-based traffic control. Movement-based traffic control can be treated as updated versions of the current intersection signal control with the incorporation of the performance of autonomous vehicle functions. Meanwhile, vehicle-based traffic control utilizes some brand-new methods, mostly in real-time fashion, to organize traffic at intersections for safe and efficient vehicle passages. However, to date, no systematic comparison between these two control categories has been performed to suggest their advantages and disadvantages. This paper conducts a series of numerical simulations under various traffic scenarios to perform a fair comparison of their performances. Specifically, we allow trajectory adjustments of incoming vehicles under movement-based traffic control, while for its vehicle-based counterpart, we implement two strategies, i.e., the first-come-first-serve strategy and the optimization-based strategy. Overall, the simulation results show that vehicle-based traffic control generally incurs a negligible delay when traffic demand is low but lead to an excessive queuing time as the traffic volume becomes high. We also discover that the comparison results can be influenced by other factors such as intersection layouts, traffic distribution and maturity of the autonomous driving technologies.

Pickup and delivery problems with late penalties can be adopted to model a wide range of practical situations in the field of transportation and logistics. However, the restrictions on the multiple vehicles’ service sequences and non-linearity caused by the late penalties make it time-consuming to solve this problem. To overcome this difficulty, we propose a novel reinforcement learning framework inspired by transformer architecture to generate tours instantly after offline training. This framework, as trained through the policy gradient method, consists of the information encoder process which can extract the coupling relationships among the pickup and delivery customers, and the decoder process with multi-vehicle attention network to allocate reasonable orders to each vehicle. Validated on Sioux Falls network, the proposed method yields the improvement of 2.4%-8.0% on the solution quality compared with Google OR-Tools and several heuristic algorithms. Notably, the baselines require dozens of minutes to achieve a lesser result on the case with 100 customers while the well-trained model based on our method can be deployed to provide a high-quality solution within seconds. Furthermore, the proposed model also shows good generalization ability in different scenarios with various scale problems, and the obtained results are shown to be quite robust to counter the fluctuation of travel time.

Network design problems (NDPs) have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem (MNDP) more complicated and difficult to solve. We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuous, discrete, and mixed-integer). We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.

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.