Comparative study on mode split discrete choice models

Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.

Dwell time estimation models for bus rapid transit stations

Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.