Detection of Road Traffic Anomalies Based on Computational Data Science

The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

Spatial-Temporal Flows-Adaptive Street Layout Control Using Reinforcement Learning

Complete streets scheme makes seminal contributions to securing the basic public right-of-way (ROW), improving road safety, and maintaining high traffic efficiency for all modes of commute. However, such a popular street design paradigm also faces endogenous pressures like the appeal to a more balanced ROW for non-vehicular users. In addition, the deployment of Autonomous Vehicle (AV) mobility is likely to challenge the conventional use of the street space as well as this scheme. Previous studies have invented automated control techniques for specific road management issues, such as traffic light control and lane management. Whereas models and algorithms that dynamically calibrate the ROW of road space corresponding to travel demands and place-making requirements still represent a research gap. This study proposes a novel optimal control method that decides the ROW of road space assigned to driveways and sidewalks in real-time. To solve this optimal control task, a reinforcement learning method is introduced that employs a microscopic traffic simulator, namely SUMO, as its environment. The model was trained for 150 episodes using a four-legged intersection and joint AVs-pedestrian travel demands of a day. Results evidenced the effectiveness of the model in both symmetric and asymmetric road settings. After being trained by 150 episodes, our proposed model significantly increased its comprehensive reward of both pedestrians and vehicular traffic efficiency and sidewalk ratio by 10.39%. Decisions on the balanced ROW are optimised as 90.16% of the edges decrease the driveways supply and raise sidewalk shares by approximately 9%. Moreover, during 18.22% of the tested time slots, a lane-width equivalent space is shifted from driveways to sidewalks, minimising the travel costs for both an AV fleet and pedestrians. Our study primarily contributes to the modelling architecture and algorithms concerning centralised and real-time ROW management. Prospective applications out of this method are likely to facilitate AV mobility-oriented road management and pedestrian-friendly street space design in the near future.

An Intersection Platoon Speed Control Model Considering Traffic Efficiency and Energy Consumption in CVIS

This paper proposed an intersection platoon speed control model considering traffic efficiency and energy consumption in the cooperative vehicle-infrastructure systems (CVIS) environment. This model divides the control situation in detail according to the different state of signal lights at the intersection and splits the platoon that cannot pass the intersection completely. The optimization model is established by taking the traffic delay and energy consumption of the platoon as the control objectives, and the model is solved by using a genetic algorithm (GA). Finally, the simulation platform is built by SUMO traffic simulation software, MATLAB, and Python to verify the model. The simulation results show that the total number of queued vehicles, the maximum number of queued vehicles, and the mean travel time of vehicles decreased by 77.81%, 33.33%, and 10.95%, respectively. Besides, the total fuel consumption is reduced by 19.95%, the total emissions of CO2, CO, HC, NOx, and PMx decreased by 19.96%, 58.55%, 51.33%, 23.81%, and 37.51%, respectively. It indicates that the proposed platoon speed control model can effectively improve traffic efficiency while reducing energy consumption and pollutant emissions.

Freeway ramp metering based on PSO-PID control

Ramp metering on freeway is one of the effective methods to alleviate traffic congestion. This paper advances the field of freeway ramp metering by introducing an application to the on-ramp, capitalizing on the macro traffic follow theory and improved the freeway traffic flow. The Particle Swarm Optimization (PSO) based on Proportional Integral Derivative (PID) controller is further developed to single ramp metering as well as to optimize the PID parameters. The approach is applied to a case study of the Changyi Freeway(G5513) in Hunan, China. The simulation is conducted by applying the actual profile traffic data to PID controller to adjust the entering traffic flow on the freeway on-ramp. The results show that the PSO-PID controller tends to converge in about 80 minutes, and the density tends to be stable after 240 iterations. The system has smaller oscillation, more accurate adjustment of ramp regulation rate, and more ideal expected traffic flow density. The traffic congestion on mainline is effectively slowed down, traffic efficiency is improved, and travel time and cost are reduced. The nonlinear processing ability of PSO-PID controller overcomes the defects of the traditional manual closing ramp, and can be successfully applied in the field of intelligent ramp metering.

Research on Platoon Dispersion Delay of Traffic Flow considering Coordinated Control

To improve the traffic capacity and reduce the delay of signalized intersections, the delay of coordinated control intersections is studied. Based on the freedom and randomness of the speed change and considering the delay problem caused by the discrete behavior, the authors deduced a new delay model. Firstly, by analyzing the kinematic behavior of traffic flow under coordinated control, it is found that traffic flow reaches the downstream intersection in two different forms. The two forms were as follows: the tail vehicles of discrete traffic flow were truncated and the front vehicles of discrete traffic flow were stopped. Then, the authors deduced the new delay model by analyzing the two conditions. Finally, the delay of the two cases is analyzed, which can be used as the basis for setting the phase difference between coordinated control intersections. The correctness of the model is verified by designing two example coordinated control intersections under unsaturated flow with MATLAB. Results show that the discrete traffic flows will have different impacts on delay or traffic efficiency when they arrive at the downstream intersection in different forms. Through the analysis of the delay of vehicles, when the green split is less than 0.64, the tail truncation delay is greater than the front truncation delay. When the green split is greater than or equal to 0.64, the opposite is true. The phase difference of upstream and downstream intersections can be optimized and coordinated according to the goal that vehicles can smoothly pass through the coordinated control intersection or ensure the minimum delay, so as to give full play to the space-time utilization of the coordinated control intersection.

Designing Maps for Bicycling Simulator Studies – three practical Approaches

Bicycle simulator studies result from attempts of solving various novel problem statements of modern transportation-related research questions. Examples imply the evaluation of novel traffic control strategies for prioritizing urban bicycle traffic, novel bicycle infrastructure (such as bicycle highways) and the interaction and communication of vulnerable road users with automated or autonomous vehicles. As one of classical disciplines of transportation research, namely traffic engineering, and less related to human factors research, automotive research, geography, urban planning or citizen science, we want to point out those bicycle simulator studies design approaches, which are more related to testing novel traffic control strategies for cyclists, experiencing changing traffic-efficiency and –safety-related parameters in ongoing interfaced microscopic traffic flow simulations. We believe that this is a key factor in experiencing various traffic situations and the evaluation of thereof. In this research, we introduce three practical approaches of how to design maps for bicycling simulator studies. This is mainly resulting from manifold practical experiences from already conducted simulator studies beginning from the year 2018.