PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

Traffic control optimization strategy based on license plate recognition data

Traffic signal control is essential to the efficiency of the road network’s operation.In recent years,more and more detailed detection data provide potential data support for traffic signal control,such as license plate recognition(LPR)data.This study aims to develop a traffic signal control optimization method based on model predictive control(MPC)and LPR data.The proposed framework of a closed-loop control system is described in detail.First,the control objectives and queue prediction model for signalized intersection are determined.Then,online optimization and feedback compensation are discussed and implemented.Calculations of the arrival rate at the downstream are based on the LPR data detected at the upstream intersection,and dynamic optimization method of the offset is proposed for a coordinated control.The model is validated using the LPR data of two consecutive intersections with a traffic simulation platform.Results demonstrate that the model can restrain extreme long queuing,improve intersection capacity,and reduce intersection average delay.The developed model promotes the system operating efficiency and shows the general advantage of real-time optimization,feedback,and control.The proposed framework can be potentially applied by local traffic management centers to improve the quality of traffic signal control.

Uncovering the CO_(2)emissions of vehicles:A well-to-wheel approach

Carbon dioxide(CO_(2))from road traffic is a non-negligible part of global greenhouse gas(GHG)emissions,and it is a challenge for the world today to accurately estimate road traffic CO_(2)emissions and formulate effective emission reduction policies.Current emission inventories for vehicles have either low-resolution,or limited coverage,and they have not adequately focused on the CO_(2)emission produced by new energy vehicles(NEV)considering fuel life cycle.To fill the research gap,this paper proposed a framework of a high-resolution well-to-wheel(WTW)CO_(2)emission estimation for a full sample of vehicles and revealed the unique CO_(2)emission characteristics of different categories of vehicles combined with vehicle behavior.Based on this,the spatiotemporal characteristics and influencing factors of CO_(2)emissions were analyzed with the geographical and temporal weighted regression(GTWR)model.Finally,the CO_(2)emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies.The results show that the distribution of vehicle CO_(2)emissions shows obvious heterogeneity in time,space,and vehicle category.By simply adjusting the existing NEV promotion policy,the emission reduction effect can be improved by 6.5%-13.5%under the same NEV penetration.If combined with changes in power generation structure,it can further release the emission reduction potential of NEVs,which can reduce the current CO_(2)emissions by 78.1%in the optimal scenario.