Signalized intersections are equipped with advance and stop-bar detectors that detect vehicles at discrete locations without linking or reidentifying them over the approach area. Accurate tracking and reidentification of vehicles between these detectors could provide valuable driver behavior data, especially during the safety-critical yellow onset periods. However, reidentifying vehicles using non-visual detection data is challenging and not well-explored, with existing analytical models relying on a priori-calibrated parameters. To this end, we propose a machine learning (ML)-based reidentification framework for accurately tracking vehicles over the advance and stop bar loop detectors. The framework comprises two major components: advanced ML and deep learning (DL) models for accurately predicting the travel time between detectors and a novel optimization model that utilizes these predicted travel times and actuation events for reidentifying vehicles. Tests carried out on a major intersection approach in Phoenix, Arizona, showed that the optimization framework based on neural oblivious decision ensemble (NODE) reidentified vehicles even at congested conditions with 94.5% precision and 92.1% recall, outperforming state-of-the-art analytical, conventional ML, and comparable DL models. The low false alarm rate and high recall of this reidentification framework open avenues for obtaining valuable driver behavior data at the yellow onset to analyze stop/go behavior, dilemma zone entry/exit, red light running, and crossing conflicts at signalized intersections.

Traffic flow forecasting is the basis for the dynamic control and application of Intelligent Transport Systems (ITS), which is of great practical importance in reducing road congestion. Accurately predicting traffic flow remains a significant obstacle due to the intricate spatial and temporal dependencies associated with traffic movement. To capture the simultaneous dynamics of traffic flow’s spatiotemporal features, this paper suggests a novel Spatiotemporal Interactive Dynamic Synchronous Graph Convolution Network (STIDSG) for traffic flow forecasting, which is composed of the Interactive Dynamic Graph Convolution Network (IDGCN) and the Spatiotemporal Synchronous Graph Convolution Layer (STSGCL). IDGCN learns and shares features using an interactive learning strategy that is based on Dynamic Graph Convolution (DGCN). By incorporating DGCN into the interactive learning structure, it is possible to learn dynamic spatial features of traffic flow in an interactive manner while also capturing temporal dependencies. STSGCL effectively captures the intricate local and dynamic correlations of traffic flows by designing numerous Spatiotemporal Synchronization Graph Convolution Modules (STSGCM) for different time periods. The results of the experiment demonstrate that the STIDSG model suggested in this paper can proficiently extract the changing spatiotemporal features of the traffic flow and outperforms the commonly used baseline methods for prediction. The results of the experiment demonstrate that the STIDSG model suggested in this paper can proficiently extract the changing spatiotemporal features of the traffic flow and outperforms the commonly used baseline methods for prediction.

Short-term passenger flow prediction is crucial for urban traffic management and crowd control. However, traditional graph neural network methods usually use static adjacency matrices in urban rail transit prediction, ignoring the dynamic changes of inter-station correlations throughout the day. To address this challenge, we propose a new model called Dynamic Multigraph Fusion Spatio-Temporal Graph Convolutional Network (DMSTGCN). This model uses Tucker decomposition to construct a time-varying dynamic graph, capturing the spatial correlations of passenger flow at different time intervals and overcoming the limitations of static matrices in previous studies while effectively mitigating overfitting. Three predefined graphs, namely the physical structure graph, the passenger flow similarity graph and the OD correlation graph, are also integrated to form the fusion graph convolution module, which further enriches the multivariate spatial correlation of passenger flows. Meanwhile, the causal temporal multi-head self-attention mechanism extracts temporal correlations, working with the fusion graph convolution module in a spatio-temporal learner for multilayer spatio-temporal feature extraction. Finally, experiments on three real-world datasets prove the excellence of DMSTGCN and offer interpretable dynamic spatial relationships.

Optimizing school bus routes is essential for improving student travel efficiency and safety, yet it presents significant challenges due to the stochastic nature of student demand. Traditional approaches often struggle to fully utilize bus capacity while adapting to daily fluctuations in demand. In response to these challenges, this paper provides a new algorithm for the school bus routing problem with stochastic demand, incorporating industrial process flexibility. Firstly, an open chain k-overlapped-route network is presented, allowing for route overlaps to fully utilize bus capacity. Secondly, a new recourse algorithm is developed, which introduces partial overlaps in bus fleet routes and employs recourse actions to determine the start and end depots for each bus. This algorithm can adapt to daily fluctuations in student demand, while ensuring that school buses operate normally on familiar routes. Finally, numerical and empirical studies validate the feasibility and applicability of the proposed algorithm, demonstrating its superiority in optimizing both the number of school buses used and the total travel distance.

Intersection/junction geometry, a crucial element in the Model Inventory of Roadway Elements (MIRE) Fundamental Data Elements (FDE), is required by the Federal Highway Administration (FHWA) to be comprehensively collected by all states by 2026. This element is not only vital for compliance with FHWA mandates but also significantly impacts traffic and demand management research. To assist the Nevada Department of Transportation (NDOT) in collecting this data for the entire state of Nevada, this article proposes an innovative framework, relying solely on the state’s road network data. Our method involves extracting potential intersection points and their location information from the road network using a suite of geoprocessing tools in GIS, followed by acquiring corresponding road map images from network providers. These maps are then processed through the YOLOv5 model for intersection detection and classification, and the results are enhanced further by several postprocessing steps. Our approach, in stark contrast to traditional methods like manual collection or from vehicle-mounted sensors (camera or LiDAR), enables the rapid extraction of statewide data in just a few hours, significantly reducing the time required and overcoming the challenges associated with complex or large intersections. The precision of the YOLOv5 model has been validated, and a comparison with NDOT’s existing partial state intersection records shows our method to be highly promising. It not only streamlines the collection process for MIRE FDE elements but also provides substantial assistance in updating existing state records.

The issue of mobility from disadvantaged areas to places of interest for work, health care, education, or entertainment poses specific challenges that cannot be approached under the same prism as that of well-resourced areas. In such areas, commuters are often captive of available transportation modes. However, very few studies have focused on identifying the key factors that influence the satisfaction of these commuters. In this paper, we introduce an agent based modeling and simulation approach, to identify and classify these factors. We show, for the case study of a township in South Africa, that speed and quality of the infrastructure are crucial factors, while waiting time and accessibility are to be improved; safety and travel time being to be watched. Then recommendations are provided to improve the service according to these factors.

Understanding the socioeconomic characteristics of passengers is crucial for intelligent services for subway systems. Nevertheless, obtaining such information poses challenges, especially sensitive attributes such as age, due to heightened considerations for data privacy. To address this challenge, we explore the correlation between travel features and age from both macro and micro perspectives and propose an improved Generative Adversarial Network (GAN) for age inference in subway contexts. In the macro level, we extract statistical features from Automatic Fare Collection data and novel features from Points of Interest data. The novel features allow us to examine the relationship between built environment and age. In the micro level, we introduce a comprehensive travel chain, considering the dynamic evolution of passenger trips during multiple days. To address the sample imbalance across age groups, we propose an improved GAN-based framework to infer the age attribute. Our framework incorporates Inner Product to capture interdependency among macro features and employs a Transformer-based module to extract sequential and spatiotemporal features within micro-level travel chains. Finally, the framework is validated using a case study of Guangzhou subway. The results indicate the superiority of our model over baseline approaches like Random Forest, Bayesian Networks, and Variational Autoencoders, showcasing accuracy improvements of 17.75%, 20.29%, and 16.10%, respectively. The use of Inner Product and Transformer-based modules enhances the predictive accuracy of the model. The utilization of travel chains proves to be effective in capturing travel features and improves the model’s accuracy in age inference. Furthermore, we delve into potential applications.

Many commuting arterials often suffer from queue spillovers and excessive delays in the commuting direction of heavy flows but underuse the roadway capacity in the opposing direction. Existing models for arterial signal control, despite their effectiveness, are not customized for such congested traffic systems with highly asymmetric flows. As such, this study proposes a customized signal optimization model with the objective of minimizing delays for the congested direction while ensuring the maximum progression for the opposite low-volume traffic. With the embedded formulations to reflect the impacts of upstream traffic streams’ arriving rates and sequence on each intersection approach’s resulting queue formation pattern, the proposed model can produce the optimal signal offsets and phase sequence to minimize the total delay in the high-volume direction. The proposed model’s explicit optimization of the arriving sequence for upstream traffic streams with the optimal phasing plan can also hold back the turning bay blockage’s onset time and duration, if inevitable, and thus reduce the total incurred delay. Performance comparison results with three benchmark models under extensive experimental scenarios have confirmed the promising properties of the proposed model, customized to contend with congested arterials plagued by saturated flows, excessive delays, and queue overflows for traffic in the commuting direction.

Accurately monitoring passenger demand fluctuations is crucial for streamlined operations of subway systems and informed decision-making. This study presents a detailed Time Series Analysis of the Toronto subway system using Wi-Fi data connection from devices as a predictor of passenger volume. Various time series models were tested for short-term forecasting, including Linear Regression, Exponential Smoothing, ARIMA, Random Forest, N-BEATS, and T-GCN. An end-to-end modeling implementation process was carried out, and the performance of each model was evaluated. The primary objective was to assess the effectiveness of short-term prediction models for univariate time series at the system level and discuss deployment challenges. While conventional time series models are fast to implement and interpretable, they require a more in-depth data exploration phase for validation, making scaling at the system level difficult. Additionally, maintenance is more challenging with conventional models, and their exploratory analysis phases need to be repeated when the models degrade over time. Prediction difficulty varied across each subway station, indicating the need for a more thorough calibration or hybrid approach, especially for transfer stations. Despite the different uses and qualities of each model in our scenario, Random Forest and Exponential Smoothing emerged as the best performers and could be a satisfactory option for robust demand forecasting at the system level.

Driverless vehicles have been attracting the attention of many researchers due to their potential positive impacts on current travel patterns and transport sustainability. In a regional context, driverless demand-responsive transit (DRT) systems are expected to benefit both passengers and operators, currently affected by low service levels and cost inefficiencies. Low-cost DRT systems may enable lower fares and consequently attract more users. The use of a hub-and-spoke (H&S) DRT solution can be viewed as a cheaper alternative to point-to-point (without the use of hubs) services, though there is an absence of literature contributions comparing DRT systems with these two designs. In this paper, we perform a preliminary assessment on how a H&S design can impact the performance of a driverless DRT regional system, considering the perspectives of the system’s operator and of its passengers. For this purpose, we rely on a methodological approach through which a H&S design based on a hub location model is compared to a point-to-point design. The approach is applied to analyze system operations in a region in central Portugal. The results indicate that a H&S design can increase the profits involved in the operation of the system, especially when compared to a point-to-point system without ride-pooling. However, profit increases come with a cost for passengers in terms of travel times and distances.