Shou Ren Hu

ESTIMATION OF DYNAMIC ASSIGNMENT MATRICES AND OD DEMANDS USING ADAPTIVE KALMAN FILTERING

The purpose of this research was to develop a dynamic model for the on-line estimation and prediction of freeway users’ origin-destination (OD) matrices. In this paper, we present a Kalman Filtering algorithm that uses time-varying assignment matrices generated by using a mesoscopic traffic simulator. The use of a traffic simulator to predict time-varying travel time model parameters was shown to be promising for the determination of dynamic OD matrices for a freeway system. Moreover, the issues of using time-varying model parameters, effects of incorporating different sources of measurements and the use of adaptive estimation are addressed and investigated in this research.

Dynamic Estimation and Prediction of Freeway O-D Matrices with Route Switching Considerations and Time-Dependent Model Parameters

An enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway origin-and-destination (O-D) matrices is presented. The effects of traffic congestion and traffic diversion information on the O-D distribution pattern are explicitly captured through a behavioral model of route switching. Moreover, in view of the time-varying nature of traffic variables, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation and prediction of dynamic freeway O-D demands.

Vehicle Detector Deployment Strategies for the Estimation of Network Origin–Destination Demands Using Partial Link Traffic Counts

Making valid inferences about network origin-destination (OD) demands from limited link traffic count data requires a carefully structured data-collection strategy. Determination of the required number of vehicle detectors (VDs) and their installation in strategic locations for OD estimation purposes is essentially a network location problem. In this paper, a mathematical programming framework based on a user equilibrium assumption was developed to determine the most desirable locations for the deployment of VDs. A linearly independent model was also developed to deal with the network location problem. Proposed models were evaluated under different network sizes and varying OD demand levels. Numerical results are explained. These findings can beneficially contribute to the preparation of a desirable VD deployment plan in a general network.

Assessing Casualty Risk of Railroad-Grade Crossing Crashes Using Zero-Inflated Poisson Models

A railroad grade crossing (RGC) is a spatial location where rail and highway users share the right-of-way. A significant number of traffic crashes and severe consequences at RGCs have signaled the need for appropriate models to investigate the key factors associated with the casualty risk level at an RGC in terms of the number of fatalities or injuries caused by one or more crashes in a specific time period. This study used a zero-inflated Poisson regression model to describe the relationship between the extra-zero count fatality or injury data and explanatory variables collected at 592 RGCs in Taiwan. The annual averaged daily traffic and the presence of Guidance Sign 31 were significantly associated with the probability of no fatality or injury encountered at an RGC; if an RGC was at risk of a fatality or injury, the number of daily trains, crossing angle, and Guidance Sign 31 significantly influenced the expected total number of fatalities or injuries caused by traffic crashes. The empirical results indicated that traffic exposure and traffic signage have significant effects on the risk levels of casualties at an RGC.

Integrated determination of network origin-destination trip matrix and heterogeneous sensor selection and location strategy

This paper proposes a two-stage optimization model to determine the origin-destination (O-D) trip matrix and the heterogeneous sensor deployment strategy in an integrated manner for a vehicular traffic network using sensor information from active (camera-based license plate recognition) and passive (vehicle detector) sensors. The first stage solves the heterogeneous sensor selection and location problem to determine the optimal sensor deployment strategy, in terms of the selection of the numbers of the two sensor types and their installation locations, to maximize the traffic information available for the O-D matrix estimation problem. The traffic information includes the observed link flow, path trajectory, and path coverage information. The second stage leverages this traffic information to determine the network O-D matrix that minimizes the error between the observed and estimated traffic flows (link, O-D, and/or path). Correspondingly, two network O-D matrix estimation models are proposed where the link-based model incorporates the flow conservation rule between O-D and link flows and uses the link-node incidence matrix, and the path-based model assumes a given link-path incidence matrix. An iterative solution procedure is designed to determine the network O-D matrix and link flow estimates. Results from numerical experiments suggest that the path-based model outperforms the link-based model in the estimation of network O-D matrices. The relative contributions of combinations of the two sensor types to the network O-D matrix estimation problem are also analyzed. They suggest that active sensors provide valuable path information to solve the O-D matrix estimation problem, but at the cost of a significantly higher unit price. The study results have key implications for heterogeneous sensor selection and location strategies.

Effect of train arrival time on crash frequency at highway-railroad grade crossings: general classification regression model

With the use of a general classification regression model, this study investigated the causal relationship between time to train arrival (TTA) and crash frequency at highway–railroad grade crossings. In particular, a stratified structure in the explanatory variables was used to avoid the collinearity problem generally confronted in linear regression models. TTA is a good estimate of rail sight distance and time to collision, and it could be used to predict crash frequency at a grade crossing. A 14-year crash data set accompanied by crossing inventory data including TTAs was collected for the empirical study. Study results indicated that a negative relationship between TTAs and crash frequencies was generally found for all types of trains. Similar causal relationships were also found in various combinations of both crossing attributes and crash characteristics. Sensitivity analysis on the variable combinations was also conducted to investigate the key risk factors that might result in traffic collisions at grade crossings. Policy implications based on the empirical study are discussed, and future research directions are recommended.

Dynamic estimation of freeway origin-destination demand and travel time using extended Kalman filtering algorithm

In the present research, a nonlinear Kalman filtering approach, i.e., extended Kalman filter (EKF) was proposed to solve dynamic OD flows and travel times on a freeway segment. The non-linearity results from the facts that the coefficient matrices in the measurement equation of the Kalman filtering framework are unknown in advance and needed to be obtained/updated in light of the most recent observations. The numerical results demonstrated the capability of the proposed EKF model in the dynamic estimation of freeway OD demands and travel times. More significantly, one can design beneficial traffic control and management strategies in accordance with the estimation results.

An optimal location model for a bicycle sharing program with truck dispatching consideration

In Taiwan, to establish a friendly environment as a green city is becoming an important issue and policy for each district. Kaohsiung City is the first city that promotes Bicycle Sharing System (BSS) in Taiwan. The City-bike system was established by the Kaohsiung City government and operated by the Kaohsiung Rapid Transit Corporation (KRTC) since 2009. For a sustainable development, a BSS operator should consider operation efficiency in internal work, such as cost deployment, human resource management and truck dispatching strategy. In this study, we use mathematical programming to develop a location model for finding an optimal location for bike renting and truck dispatching where the main objective is to minimize the total system cost of fixed cost, operating cost, passenger travel cost and dispatch routing cost. The ultimate goal is to increase the KRTCs ridership by providing transit users with convenient first- and/or last-mile feeder services.

Optimizing Headways for Mass Rapid Transit Services

Mass rapid transit (MRT) operators need to balance the total system costs with the level of service. The primary service of MRT systems is to provide cost-efficient mobility. Governments are responsible for establishing and regulating minimum standards to ensure that the MRT service indicators meet a specific level. Thus, approaches to optimizing MRT system service frequencies are critical concerns for MRT system operators. Previous studies on MRT operations have focused primarily on delays, energy conservation, route design, and general system operations. Conversely, few studies have focused on optimizing headway problems. This study adopts a mathematical programming method to develop a headway-oriented model for the Kaohsiung MRT (KMRT) system aiming to minimize system costs while maintaining an acceptable level of train services. The developed model systematically adjusts train headways based on time-series passengers’ spatio-temporal distribution data. A numerical case study and sensitivity analysis were conducted to test the feasibility and effectiveness of the proposed models and solution algorithms. The proposed model framework provided the KMRT operator with a flexible tool to evaluate the effects of service frequency on both operating cost and passengers’ waiting cost, which facilitates the operator to prepare a cost-efficient train service plan.

Model crash frequency at highway–railroad grade crossings using negative binomial regression

Despite the fact that traffic collisions at highway–railroad grade crossings (HRGXs) are rare events, the impact of HRGX crashes is nevertheless more severe than highway crashes. Empirical studies show that traffic collisions at HRGXs are mainly attributed to railway-related and/or highway-related characteristics, particularly drivers’ abnormal behavior, driving around, or through an HRGX. These factors have different effects on crash likelihood (i.e., the number of traffic collisions or crash frequency) at an HRGX. To explore the causal relationship between crash frequency and the factors related to railroad and highway systems, we used a negative binomial regression model to identify the factors that are statistically significantly associated with traffic collisions at HRGXs, and conducted relevant sensitivity analyses to investigate the marginal effect of daily highway traffic on changes in crash frequency. The empirical study shows that the number of daily trains, the number of tracks, highway separation, annual averaged daily traffic (AADT), and crossing length had statistically significant effects on the mean number of traffic collisions (all p-values ≤ 0.0487). Further, the marginal effect of the AADT on the change of crash frequency indicates that crash likelihood monotonically increases with the increase of AADT.