Zhiyuan Liu

Exploring the Mode Change Behavior of Park-and-Ride Users

This paper investigates the mode change behavior of park-and-ride (P&R) users, which is of considerable significance to analyze the effectiveness of P&R site on the commuters’ travel mode change as well as the increase of public transport mode share. Data from an intercept interview survey conducted at different P&R facilities in Metropolitan Melbourne is used. A questionnaire containing revealed preference (RP) and stated preference (SP) questions is used to interview the individuals who park at the facility and catch public transport to go to city. This study firstly aims to know the factors affecting current travel behavior using RP data and secondly to investigate the importance of the factors on influencing the commuters’ decision of travel mode choice using the SP data. The empirical models using multinomial logistic regression reveal that travel time taken by transit vehicle and transfer time at P&R stations are the primary factors affecting individuals’ decision on choosing public transport whereas parking fare is the additional factor affecting commuters’ choice of driving. Based on the results of this study, the effectiveness of P&R scheme on commuters’ travel mode change is evaluated which would be helpful to shed lights on the future construction of P&R sites.

A Big Data Approach for Clustering and Calibration of Link Fundamental Diagrams for Large-Scale Network Simulation Applications

Existing methods for calibrating link fundamental diagrams (FDs) often focus on a limited number of links and use grouping strategies that are largely dependent on roadway physical attributes alone. In this study, we propose a big data-driven two-stage clustering framework to calibrate link FDs for freeway networks. The first stage captures, under normal traffic state, the variations of link FDs over multiple days based on which links are clustered in the second stage. Two methods, i.e. the standard k-means algorithm combined with hierarchical clustering and a modified hierarchical clustering based on the Fréchet distance, are applied in the first stage to obtain the FD parameter matrix for each link. The calibrated matrices are input into the second stage where the modified hierarchical clustering is reemployed as a static approach resulting in multiple clusters of links. To further consider the variations of link FDs, the static approach is extended by modifying the similarity measure through the principle component analysis (PCA). The resulting multivariate time-series clustering models the distributions of the FD parameters as a dynamic approach. The proposed framework is applied on the Melbourne freeway network using one-year worth of loop detector data. Results have shown that (a) similar roadway physical attributes do not necessarily result in similar link FDs, (b) the connectivity-based approach performs better in clustering link FDs as compared with the centroid-based approach, and (c) the proposed framework helps achieving a better understanding of the spatial distribution of links with similar FDs and the associated variations and distributions of the FD parameters.

Calibration of traffic flow fundamental diagrams for network simulation applications: A two-stage clustering approach

This paper aims to propose a two-stage clustering approach for calibration of traffic flow fundamental diagrams for dynamic traffic assignment (DTA) simulations. Unlike previous research efforts focusing on supervised grouping strategies that are largely dependent on roadway physical attributes, a data-driven perspective is explored using big traffic data. The two-regime modified Greenshields traffic flow model is used to fit the historical observations on a daily basis using the non-linear least squares method. A two-stage clustering approach is proposed based on the calibrated models where the first stage aims to capture day-to-day variations in traffic flow fundamental diagrams while the second stage aims to aggregate links with similar traffic flow characteristics. The standard k-means algorithm is applied in the first stage and a modified hierarchical clustering based on the Fréchet distance is proposed in the second stage. The calibrated and clustered results highlight the feasibility and the effectiveness of the proposed approach.

Park-and-Ride Network Design in a Bi-Modal Transport Network to Prompt Public Transport Mode Share

Park and Ride (P&R) is regarded as a means of encouraging car drivers to shift to public transport modes for part of their journey. Since the first siting of P&R in the 1930s, various levels of success have been experienced in different cities around the world. It has been well recognized that the location and the corresponding parking size are the key factors for the performance of the P&R system. This paper proposes a bi-level mathematical programming model which is a mathematical expression of Stackelberg duopolistic game to determine the best location for each P&R car park, with the objective of prompting the public transport mode share. In the model, the network users route choice behavior is assumed to follow user equilibrium with elastic demand. A solution algorithm is proposed for solving this model.