Real-time urban traffic amount prediction models for dynamic route guidance systems

Abstract

The route guidance system (RGS) has been considered an important technology to mitigate urban traffic congestion. However, existing RGSs provide only route guidance after congestion happens. This reactive strategy imposes a strong limitation on the potential contribution of current RGS to the performance improvement of a traffic network. Thus, a proactive RGS based on congestion prediction is considered essential to improve the effectiveness of RGS. The problem of congestion prediction is translated into traffic amount (i.e. the number of vehicles on the individual roads) prediction, as the latter is a straightforward indicator of the former. We thereby propose two urban traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road links. These two models are then applied to construct a centralized proactive RGS. Evaluation results show that (1) both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model, (2) providing proactive route guidance helps reduce average travel time by up to 70% compared to providing reactive one and (3) non-rerouted vehicles could benefit more from route guidance than rerouted vehicles do.

A system for the generation of synthetic Wide Area Aerial surveillance imagery

“…In this work, the open source traffic simulator known as 'Simulation of Urban MObility' or 'SUMO' has been used and adapted to reflect the needs of the WAAS simulation [21,22]. SUMO has been available since 2001 and was developed by the Institute of Transportation Systems at the Deutsches Zentrum für Luft und Raumfahrt (DLR) for evaluating modifications to road infrastructure and transport policy, examples are: optimizing traffic light timings [26], forecasting traffic density [27,28] and evaluating wireless in vehicle systems known as "Vehicle-to-X" (V2X) infrastructure [29] .…”

Horizontally scalable probabilistic generalized suffix tree (PGST) based route prediction using map data and GPS traces

“…Horizontal scalability aspect is not taken care. Authors in [36] proposed urban traffic amount prediction for route guidance systems. Prediction is based on spatiotemporal correlation of the road network.…”

Improve Traffic Management in the Vehicular Ad Hoc Networks by Combining Ant Colony Algorithm and Fuzzy System

“…Reference [13] includes two models for Route Guidance Systems (RGS), one based on propagating traffic flow in the network and another based on tow flow capacity at different times on related road links. They claim that both models reduce prediction error to 52% and travel time average to 70% in comparison to other methods.…”