Abstract

PUBLISHED Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travelers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their pervasive adoption. This paper presents a system for estimating vehicle location based on information generated by data sources typically integrated within existing ITS platforms. This enables location estimation for public transport vehicles without the need for deploying a designated sensor infrastructure in each vehicle, thereby reducing deployment and maintenance cost significantly. A prototypical vehicle location estimation system has been realized as part of and using data provided by the iTransIT ITS framework. Initial evaluation results show that such a system is feasible in a distributed manner and that estimated results are within 20% compared to empirical data. The work described in this paper was partly supported by Lero - the Irish Software Engineering Research Centre. SFI)

Abstract

Does consideration of average speed distributions on roads-as compared to single mean speed-lead to different results in emission modelling of large road networks? To address this question, a post-processing method is developed to predict mean speed distributions using available traffic data from a dynamic macroscopic traffic model (Indy) that was run for an actual test network (Amsterdam). Two emission models are compared: a continuous (COPERT IV) and a discrete model (VERSIT+macro). Computations show that total network emissions of CO, HC, NOx, PM10 and CO2 are generally (but not always) increased after application of the mean speed distribution method up to +9%, and even up to +24% at sub-network level (urban, rural, motorway). Conventional computation methods thus appear to produce biased results (underestimation). The magnitude and direction of the effect is a function of emission model (type), shape of the composite emission factor curve and change in the joint distribution of (sub)-network VKT (vehicle kilometres travelled) and speed. Differences between the two emission models in predicted total network emissions are generally larger, which indicates that other issues (e.g., emission model validation, model choice) are more relevant. © 2007 Elsevier Ltd. All rights reserved.