Traffic incident detection based on a global trajectory spatiotemporal map

Traffic incidents endanger the smooth running of vehicles. Congestion caused by traffic incidents has caused a waste of time and fuel and seriously affected transportation efficiency. At present, most methods use manual judgment or image features to detect traffic incidents, but these methods lack timeliness, leading to secondary incidents. For dangerous road sections such as ramp-free and long downhills, this paper proposes an algorithm to quickly detect traffic incidents based on a spatiotemporal map of vehicle trajectories. First, a vehicle dataset from the monitoring perspective is constructed, and an improved YOLOv4 detection algorithm is used to detect images organized as batches. Based on the detection result, the multi-object tracking method of vehicle speed prediction in key frames is used to obtain the vehicle trajectory. Then according to the vehicle trajectory obtained in a single scene, the vehicle trajectory is reidentified and associated in the continuous monitoring scene to construct a long-distance vehicle trajectory spatiotemporal map. Finally, according to the distribution and generation status of the trajectory in the spatiotemporal map, traffic incidents such as vehicle parking, vehicle speeding, and vehicle congestion are analyzed. Experimental results show that the proposed method greatly increases the speed of vehicle detection and tracking and obtains high mAP, MOTA, and MOTP indicators. The global spatiotemporal map constructed by trajectory reidentification can achieve high detection rates for traffic incidents, reduce the average elapsed time, and avoid the problems of the inaccuracy of analyzing image features.

Can ambulance dispatch categories discriminate traffic incidents that do/do not require a lights and sirens response?

PurposeTraffic incidents vary considerably in their severity, and the dispatch categories assigned during emergency ambulance calls aim to identify those incidents in greatest need of a lights and sirens (L&S) response. The purpose of this study was to determine whether dispatch categories could discriminate between those traffic incidents that do/do not require an L&S response.Design/methodology/approachA retrospective cohort study of ambulance records was conducted. The predictor variable was the Traffic/Transportation dispatch categories assigned by call-takers. The outcome variable was whether each incident required an L&S response. Possible thresholds for identifying dispatch categories that require an L&S response were developed. Sensitivity and specificity were calculated for each threshold.FindingsThere were 17,099 patients in 13,325 traffic incidents dispatched as Traffic/Transportation over the study period. “Possible death at scene” ‘had the highest odds (OR 22.07, 95% CI 1.06–461.46) and “no injuries” the lowest odds (OR 0.28 95% CI 0.14–0.58) of requiring an L&S response compared to the referent group. The area under the ROC curve was 0.65, 95% CI [0.64, 0.67]. It was found that Traffic/Transportation dispatch categories allocated during emergency ambulance calls had limited ability to discriminate those incidents that do/do not require an L&S response to the scene of a crash.Originality/valueThis research makes a unique contribution, as it considers traffic incidents not as a single entity but rather as a number of dispatch categories which has practical implications for those emergency medical services dispatching ambulances to the scene.

Logical Structure of Actions to Reduce the Impacts of Traffic Incidents

Traffic incidents (such as broken-down vehicles, accidents, flat tires and other) constitute an important concern in the urban context, impacting the sustainable development. Thus, currently, the proposition of efficient traffic incident management systems has been encouraged to re-establish road safety and restore the network's traffic capacity. Thus, this paper aims to investigate the main impacts of traffic incidents and elaborate a logical structure of actions that should be employed to improve their management. The results show that many impacts can be identified in the three spheres of sustainable development and improvement actions must accelerate responses to emergencies, invest in Intelligent Transportation System (ITS), develop urban planning with a focus on more roads secure and enforce existing laws and regulations.

Design, Implementation, and Evaluation of an Information System to Enhance Road Safety Level for Pedestrians in China

Pedestrians’ unsafe behavior is one of the most critical factors causing traffic incidents in China. The primary objective of this study is to explore the cause of pedestrians’ unsafe behavior and provide possible solutions. We interviewed pedestrians and experts to investigate pedestrians’ unsafe behaviors. Results from interviews indicated that pedestrians were likely to exhibit unsafe behavior at intersections owing to use of smartphones, reluctance to obey the rules, and unawareness of risk. According to the experts, attracting the attention of pedestrians and guiding them to exhibit safe behaviors can improve their safety. Based on these results, we designed “LookMe,” which is a multimedia information system placed at the intersections, to guide pedestrians across the road and improve their experience of waiting in traffic. The results of user tests indicated that pedestrians had relatively high acceptance of LookMe. Moreover, participants wanted to see diverse multimedia information on the screen of LookMe such as news, videos, maps, and traffic information. Findings from this study can be useful in understanding why Chinese pedestrians exhibit unsafe behaviors and proposing effective solutions to enhance their safety.

A Descriptive Analysis on the Impact of COVID-19 Lockdowns on Road Traffic Incidents in Sydney, Australia

COVID-19 has had tremendous effects worldwide, resulting in large-scale death and upheaval. An abundance of studies have shown that traffic patterns have changed worldwide as working from home has become dominant, with many facilities, restaurants and retail services being closed due to the lockdown orders. With regards to road safety, there have been several studies on the reduction in fatalities and crash frequencies and increase in crash severity during the lockdown period. However, no scientific evidence has been reported on the impact of COVID-19 lockdowns on traffic incident duration, a key metric for crash management. It is also unclear from the existing literature whether the impacts on traffic incidents are consistent across multiple lockdowns. This paper analyses the impact of two different COVID-19 lockdowns in Sydney, Australia, on traffic incident duration and frequency. During the first (31 March–28 April 2020) and second (26 June–31 August 2021) lockdowns, the number of incidents fell by 50% and 60%, respectively, in comparison to the same periods in 2018 and 2019. The proportion of incidents involving towing increased significantly during both lockdowns. The mean duration of crashes increased by 16% during the first lockdown, but the change was less significant during the subsequent lockdown. Crashes involving diversions, emergency services and towing saw an increase in the mean duration by 67%, 16%, and 47%, respectively, during the first lockdown. However, this was not reflected in the 2021 data, with only major crashes seeing a significant increase, i.e., by 58%. There was also a noticeable shift in the location of incidents, with more incidents recorded in suburban areas, away from the central business area. Our findings suggest drastic changes in incident characteristics, and these changes should be considered by policymakers in promoting a safer and more sustainable transportation network in the future.

Adaptive car control system based on a predictive model

An adaptive control is a control, which by pre-setting the parameters of the controller, enables the control of processes whose parameters are time-varying or are initially uncertain. The possibilities and benefits of adaptive control are versatile and can be best demonstrated by applying the system while driving a car, or maintaining the optimal speed and distance between cars, which is shown in this paper. As the car’s weight decreases while driving due to fuel consumption, the control algorithm has to be adapted to the changed driving conditions. Accordingly, an adaptive control system using the Matlab software package, and an adaptive cruise control system (ACC) was created in this paper, which is based on a predictive model. After evaluating the developed model of adaptive car motion control, the output parameters such as speed, acceleration, and distance between the two vehicles were analyzed. In this paper a PID controller is used to reduce oscillations in the system. First, the P controller was used to reduce the rise time of the significant values, then the PI controller improved the rise time, and finally the PID controller achieved overshoot reduction performance without affecting the dynamic response system. The obtained results confirm the justified expectations for the possibility of adaptive car control utilization as one of the possible solutions to the increasing traffic incidents, as well as a measure to improve the reduction of these incidents.

Understanding E-Scooter Incidents Patterns in Street Network Perspective: A Case Study of Travis County, Texas

Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in U.S. urban environments remains unexplored due to the lack of traffic and crash data related to E-scooters. Our study objective is to better understand E-scooter crashes from a street network perspective. New parcel level street network data are obtained from Zillow and curated in Geographic Information System (GIS). We conducted local Moran’s I and independent Z-test to compare where and how the street network that involves E-scooter crash differs spatially with traffic incidents. The analysis results show that there is a spatial correlation between E-scooter crashes and traffic incidents. Nevertheless, E-scooter crashes do not fully replicate characteristics of traffic incidents. Compared to traffic incidents, E-scooter incidents tend to occur adjacent to traffic signals and on primary roads.