Journal of Intelligent Transportation Systems最新文献

Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities.

Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm’s methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user’s ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion.

Short term forecasting is essential and challenging in time series data analysis for traffic flow research. A novel deep learning architecture on short-term traffic flow prediction was presented in this work. In conventional model-driven prediction method, a critical deviation in prediction accuracy was occurred in face of large fluctuations in traffic flow, while machine and deep learning-based approaches performed well in accuracy study than conventional regression-based models. Moreover, a fusion attention mechanism bidirectional long short-term memory model (ATT-BiLSTM) was proposed due to its bidirectional LSTM (BiLSTM) and attention mechanism units. The model not only dealt with forward and backward dependencies in time series data, but also integrated the attention mechanism to improve the ability on key information representation. The BiLSTM layer was exploited to capture bidirectional temporal and spatial features dependencies from historical data. The proposed model was also trained and validated using freeway toll datasets from Humen Bridge. The results showed that compared with ARIMA and SVR models, the indicators of the proposed model have been significantly improved. The ablation experiments were conducted to evaluate the role of the attention mechanism module. Compared with BiLSTM, CNN and 1DCNN-ATT-BiLSTM models, the MAE, RMSE and MAPE indexes of proposed model were reduced by 0.6–5.9%, 1.6–4.7% and 0.6–22.8%, respectively. More accurate predictions were obtained by the proposed model. The research results are of great significance to improve the level of traffic management.

The global COVID-19 pandemic has had a great impact on transportation across the United States. However, there is a lack of studies investigating the pandemic’s impact on vehicular traffic at the later stage of the pandemic. Therefore, this paper studies the change of freeway traffic patterns in two metropolitan counties in the State of Utah at the latter stage of the pandemic. We found that with the relaxation of travel restriction and the COVID vaccine, vehicular traffic has recovered to equaling, if not exceeding, pre-pandemic levels. Truck traffic is higher than the pre-pandemic level due to the growth of online shopping and on-demand delivery. To help responsive agencies to prepare for the near-future traffic pattern, a traffic prediction model based on an innovative approach integrating machine learning with graph theory is proposed. The evaluation shows that the proposed prediction model has a desirable performance. The mean absolute percentage prediction error is between 0.38% and 1.74% for different jurisdictions. On average, the modal outperforms the traditional long short-term memory model by 31.20% in terms of root mean squared prediction error.

This paper presents a design of lane change decision and control algorithm in highly dense traffic situation for self-driving vehicles using motion-based adaptive uncertainty propagation and Stochastic Model Predictive Control (SMPC). Essential ideas of the proposed algorithm are introduced; i) an optimal motion in a current situation with multiple criteria decision making (MCDM), ii) four steps to change lane successfully in the dense traffic situation which is modeled as a simple acceleration model based on real driving data, iii) motion-based adaptive uncertainty propagation to consider a model error. The proposed algorithm has been evaluated via simulation studies in MATLAB/Simulink and CARSIM. The simulation results show the effectiveness of the proposed algorithm and its performance for changing lane in the highly-dense traffic situation.

Stress and driving are a dangerous combination which can lead to crashes, as evidenced by the large number of road traffic crashes that involve stress. Therefore, it is essential to build a practical system that can classify driver stress level with high accuracy. However, the performance of such a system depends on hyperparameter optimization choices such as data segmentation (windowing hyperparameters). The configuration setting of hyperparameters, which has an enormous impact on the system performance, are typically hand-tuned while evaluating the algorithm. This tuning process is time consuming and there are also no generic optimal values for hyperparameters values. In this work, we propose a meta-heuristic approach to support automated hyperparameter optimization and provide a real-time driver stress detection system. This is the first systematic study of optimizing windowing hyperparameters based on Electrocardiogram (ECG) signal in the domain of driving safety. Our approach is to propose a framework based on Particle Swarm Optimization algorithm (PSO) to select an optimal/near optimal windowing hyperparameters values. The performance of the proposed framework is evaluated on two datasets: a public dataset (DRIVEDB dataset) and our collected dataset using an advanced simulator. DRIVEDB dataset was collected in a real-time driving scenario and our dataset was collected using an advanced driving simulator in the control environment. We demonstrate that optimizing the windowing hyperparameters yields significant improvement in terms of accuracy. The most accurate built model applied to the public dataset and our dataset, based on the selected windowing hyperparameters, achieved 92.12% and 77.78% accuracy, respectively.

This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative longitudinal Control for Merging maneuvers (CCM) strategy based on Automated Vehicles (AV) is proposed considering cooperation among vehicles, courtesy, and the coexistence of AV and Human-Driven Vehicles (HDV). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AV behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. CCM mainly requires vehicles to have automated longitudinal (such as Adaptive Cruise Control (ACC)) and lane-changing control, which are already commercially available on some vehicles. Also, it does not need 100% AV penetration, presenting itself as a promising solution for improving traffic operations in lane reduction transition areas such as highway work zones.

Shared bikes, shared e-scooters, and public transit make up most public transportation modes in big cities. Their combination can provide a convenient, efficient, and flexible multi-modal transportation service. Despite the obvious similarity among them, differences exist in the roles that they play in a multi-modal transportation system. A case study in the City of Austin, where shared bikes, shared e-scooters, and public transit coexist, is used to explore their unique characteristics and how they spatially complement or compete with each other. The results show that public transit has more pronounced characteristics related to commuting than shared micromobility modes do, and that shared bikes are more likely to be used for commuting compared to shared e-scooters. Interestingly, the results suggest that there is spatial segregation between where shared bikes complement public transit and shared e-scooters complement public transit, i.e., only one shared mode complements public transit at a given area.

Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control.

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control.