基于PSL模型的轨道车站行人路径选择研究

根据效用最大化理论,建立了path-size logit（PSL）模型和路径效用函数.以北京市地铁十号线国贸站的调查数据为基础,标定了模型参数.对模型的分析表明,行人在轨道车站售票区域的路径选择与出行目的及各设施的服务时间有关,同时,路径重复系数对路径效用的影响大,佐证了行人对拥挤路径的规避行为.     

基于Logit模型的居住区共享泊位选择行为分析

为定量描述停车者对居住区共享泊位的选择行为影响因素,将停车者的个人属性与出行属性作为效用变量,将居住区共享泊位停车和路内就近停车作为选择肢,建立二项Logit模型。分析表明：个人月收入、停车时长、额外步行距离、费率差等为影响停车者选择居住区共享泊位停车的显著影响因素,其中额外步行距离和费率差对共享泊位选择概率的影响程度更大,较低层次的个人月收入变化对共享泊位选择概率的影响更大。     

An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

Effects of providing total cost of ownership information on consumers’ intent to purchase a hybrid or plug-in electric vehicle

Energy-saving technologies have a difficult time being widely accepted in the marketplace when they have a high initial purchase price and deferred financial benefits. Consumers might not realize that, in the long-run, the financial benefits from reduced energy consumption offset much or all of the initial price premium. One strategy to address consumer misconception of this advantage is to supply information on the “total cost of ownership”, a metric which accounts for the purchase price, the cost of the fuel, and other costs over the ownership period. In this article, we investigate how providing information on five-year fuel cost savings and total cost of ownership affects the stated preferences of consumers to purchase a gasoline, conventional hybrid, plug-in hybrid, or battery electric vehicle. Through an online survey with an embedded experimental design using distinct labels, we find that respondent rankings of vehicles are unaffected by information on five-year fuel cost savings. However, adding information about total cost of ownership increases the probability that small/mid-sized car consumers express a preference to acquire a conventional hybrid, plug-in hybrid, or a battery-electric vehicle. No such effect is found for consumers of small sport utility vehicles. Our results are consistent with other findings in the behavioral economics literature and suggest that further evaluation of the effects of providing consumers with information on the total cost of vehicle ownership is warranted.     

A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data

Driver’s stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers’ stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver’s SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers’ behaviors using thousands drivers’ decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply ‘black-box’ procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver’s stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.     

Modelling the potential for cycling in access trips to bus,train and metro in Rio de Janeiro

Most studies that address the integration of cycling and public transport (PT) focus on developed countries and deal with multi-modal bicycle-train trips. Little is known about the integration of cycling and other main modes such as bus and metro, especially in developing countries, where entirely different socio-economic and trip making conditions prevail. The aim of this study is to model the propensity of current PT users to shift to the bicycle in access trips to bus stops, train and metro stations in Rio de Janeiro, Brazil. Interviews were conducted to collect data on the socio-economic characteristics of the interviewee, trip and spatial characteristics and self-reported barriers and motivators for bicycle use. Two binary logit models were estimated to predict the main factors affecting the propensity to use a bicycle as feeder mode to PT. The results show that socio-economic characteristics as well as barriers and motivators are important factors to explain propensity for bike and ride. The barriers’ model reveals that personal constraints, living too close to the PT boarding point, current parking conditions and public safety play a role. For the motivators’ model, changing home location, owning a bicycle, implementation of cycle ways and improvement in parking conditions are explanatory. Policy recommendations are formulated to increase bicycle ownership and improve cycling infrastructure.     

Investigating preference heterogeneity in Value of Time (VOT) and Value of Reliability (VOR) estimation for managed lanes

This paper presents an empirical study in investigating user heterogeneity of Value of Time (VOT) and Value of Reliability (VOR). Combined Revealed Preference (RP) and Stated Preference (SP) data were used to understand traveler choice behavior regarding the usage of managed lanes (MLs). The data were obtained from the South Florida Expressway Stated Preference Survey, which focused on automobile drivers who had traveled on the I-75, I-95, or SR 826 corridors in South Florida. Mixed logit modeling was applied and indicated an average value of $13.55 per hour for VOT and $16.13 per hour for VOR. Potential sources of heterogeneity in user sensitivities to time, reliability, and cost were identified and quantified by adding interaction effects of the variables in the mixed logit model. The findings indicated that various socioeconomic demographic characteristics and trip attributes contributed to the variations in VOT and VOR at different magnitudes. The results of this study contribute to a better understanding on what attributes lead to higher or lower VOT and VOR and to what extent. These findings can be incorporated into the demand forecasting process and lead to better estimates and enhanced analytical capabilities in various applications, such as toll feasibility studies, pricing strategy and policy evaluations, and impact analysis.     

Methodological challenges in modelling the choice of mode for a new travel alternative using binary stated choice data – The case of high speed rail in Norway

Binary stated choices between traveller’s current travel mode and a not-yet-existing mode might be used to build a forecasting model with all (current and future) travel alternatives. One challenge with this approach is the identification of the most appropriate inter-alternative error structure of the forecasting model.By critically assessing the practise of translating estimated group scale parameters into nest parameters, we illustrate the inherent limitations of such binary choice data. To overcome some of the problems, we use information from both stated and revealed choice data and propose a model with a cross-nested logit specification, which is estimated on the pooled data set.     

高速客运专线客流分担率模型及其应用研究

目前,我国客运专线已在积极修建,2008年后,高速客运专线网将逐步形成,高速客运专线的建成将大幅度提高铁路的竞争能力。本文研究客运专线的建成对通道上其他运输方式的影响,即各种方式分担率的变化。Logit模型是预测运输通道上各种运输方式客流分担率的一种比较成熟的方法,它在交通运输领域有着广泛的应用。基于此,首先研究高速客运专线客流分担率模型(Logit模型),选择经济性、快速性、方便性、舒适度、安全性为5个衡量指标,并建立其广义费用函数,利用相关研究结果和极大似然估计法,确定模型参数,最后以北京~太原间各种运输方式的竞争为例来研究该模型的应用。结果显示建成后的北京~太原间高速客运专线将吸引大量客流,使铁路客流分担率增加10%。     

Confidence intervals of willingness-to-pay for random coefficient logit models

Random coefficient logit (RCL) models containing random parameters are increasingly used for modelling travel choices. Willingness-to-pay (WTP) measures, such as the value of travel time savings (VTTS) are, in the case of RCL models estimated in preference space, ratios of random parameters. In this paper we apply the Delta method to compute the confidence intervals of such WTP measures, taking into account the variance–covariance matrix of the estimates of the distributional parameters. The same Delta method can be applied when the model is estimated in WTP space. Compared to simulation methods such as proposed by Krinsky and Robb, the Delta method is able to avoid most of the simulations by deriving partly analytical expressions for the standard errors. Examples of such computations are shown for different combinations of random distributions.