Dynamic Link Flow Estimation according to Historical Travel Times

Abstract: Vast application of ITS and the availability of numerous on-road detection devices has resulted in variety of alternative data sources to be exploited and used in the field of traffic modelling. In this thesis, historical travel times, as an alternative data source, is employed on the developed method to perform dynamic network loading. The developed method, referred to as DNLTT, uses the share of each route available in the route choice set from the initial demand, as well as link travel times to perform the network loading. The output of the algorithm is time-dependent link flows. DNLTT is applied on Stockholm transportation network, where it is expected to have variation in link travel times in different time-periods, due to network congestion. In order to calculate the route shares, a time-sliced OD matrix is used. The historical travel times and the routes in the route choice set are extracted from an existing route planning tool. An available logit model, which considers the route travel time as the only logit parameter, is used for the route share calculation and the network loading is performed according to 2 different methods of DNLTT and DL. The evaluation of results is done for a toy network, where there happen different network states in different time-periods. Furthermore, the model output from Stockholm case study is analyzed and evaluated. The dynamic behavior of DNLTT is studied by analysis of link flows in different time-periods. Furthermore, the resulting link flows from both network loading methods are compared against observed link flows from radar sensors and the statistical analysis of link flows is performed accordingly. DNLTT exhibits a better performance on the toy network compared to DL, where the increasing link travel times cause the link flows to decline in different time-periods. However, the output of the developed method does not resemble the observed link flows for the investigated links in Stockholm case study. It is strongly believed, that the performance of DNLTT on the investigated transportation network potentially improves, in case the historical travel times better resemble the network dynamics. In addition to a more reliable data set, an OD adjustment process in all the time-periods is believed to generate better model output.