Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach

In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process. While most previous map-matching methods have focused on using rule-based algorithms to deal with the map-matching problems, in this paper, we consider the map-matching task from the data-driven perspective, proposing a deep learning-based map-matching model. We build a Transformer-based map-matching model with a transfer learning approach. We generate trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of ground-truth data to minimize the model development cost and reduce the real-to-virtual gap. Three metrics (Average Hamming Distance, F-score, and BLEU) at two levels (point and segment level) are used to evaluate the model performance. The results indicate that the proposed model outperforms existing models. Furthermore, we use the attention weights of the Transformer to plot the map-matching process and find how the model matches the road segments correctly.Comment: 25 pages, 9 figures, 4 table

A Comparison Analysis of Surrogate Safety Measures with Car-Following Perspectives for Advanced Driver Assistance System

Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation. Document type: Articl

Intelligent Advisory Speed Limit Dedication in Highway Using VANET

Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle’s travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle’s characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions

Asymmetric Microscopic Driving Behavior Theory

Numerous theories on traffic have been developed as traffic congestion gains more and more interest in our daily life. To model traffic phenomena, many traffic theorists have adopted theories from other fields such as fluid mechanics and thermodynamics. However, their efforts to model the traffic at a microscopic level have not been successful yet. Therefore, to overcome the limitations of the existing theories we propose a microscopic asymmetric traffic theory based on analysis of individual vehicle trajectories. According to the proposed theory, vehicle traffic is classified into 5 phases: free flow, acceleration, deceleration, coasting, and stationary. The proposed theory suggests that traffic equilibrium exists as 2-dimensional area bounded by A-curve and D-curve, and explains phase transitions. The basic theory was extended to address driver behavior such as vehicle maneuvering error and anticipation. The proposed theory was applied to explain several traffic phenomena in congested traffic such as traffic hysteresis, capacity drop, stability, relaxation after lane change, and stop-and-go waves. We provided reasonable and intuitive explanations on these phenomena which cannot be easily understood with existing theories

Impact of Traffic States on Freeway Collision Frequency

Freeway collisions are thought to be affected by traffic states. To reduce the number of collisions, the study to reveal how the traffic states influence collisions are required. Therefore, the purpose of the paper is to suggest a method to relate traffic states to collision frequency in freeway. We first defined section- based traffic phases showing traffic state of a section using upstream and downstream traffic states: free flow (FF), back of queue (BQ), bottleneck front (BN) and congestion (CT). Secondly, by integrating freeway collision data and traffic data from the California PeMS database, over a three-year period, we obtained the collision frequency for each traffic phase, and compared for a 32mile section of the I-880 freeway. The results show that collision rate in BN, BQ, and CT phase are approximately 5 times higher than the collision rate in FF. Also, the proposed method shows potential for predicting collision frequencies on freeway sections when combined by traffic simulation