城市群轨道交通方式划分非集计logit模型

以城市群轨道交通方式划分为研究对象,根据城市群轨道交通特点,提出将城市群客流分为城区客流和城际客流,分别预测其轨道交通分担率.利用非集计模型的基础理论,选取时间、费用与个人收入的比值作为效用函数的特性变量,以交通行为调查的RP和SP联合数据为基础,建立了应用于城市群交通方式划分的非集计模型.并以长株潭城际轨道交通方式划分为例,进行了参数估计和轨道交通分担率的预测.     

The asymmetric effects of service quality on passengers' choice of carriers for international air travel

The asymmetric effects of service quality on international travelers' airline choice behavior were investigated in this paper. The linear structural equation modeling system was first applied to quantify passengers' perceptions of service quality provided by airlines, which were then used as the input variables of the asymmetric response model developed to capture international travelers' airline decision. Their asymmetric responses to various service quality of airlines were assumed to be either gains or losses with respect to their reference points. The results showed that the asymmetric response model performs better than the traditional logit models that capture only symmetrical effects.     

Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python

The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data.     

Identifying travel mode with GPS data

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.     

Reducing average comprehensive travel cost by rationally allocating trips to different travel modes

To study the effect of different transport policies on reducing the average comprehensive travel cost (CTC) of all travel modes, by increasing public transport modal share and decreasing car trips, an optimization model is developed based on travel cost utility. A nested logit model is applied to analyze trip modal split. A Genetic Algorithm is then used to determine the implementation of optimal solutions in which various transport policies are applied in order to reduce average CTC. The central urban region of Beijing is selected as the study area in this research. Different policies are analyzed for comparison, focusing on their optimal impacts on minimizing the average CTC utility of all travel modes by rationally allocating trips to different travel modes in the study area. It is found that the proposed optimization model provides a reasonable indication of the effect of policies applied.     

停车诱导系统的诱导效果分析

介绍了诱导后即有完整停车信息下的多项logit停车选择模型,其中共有4个停车场属性的影响因素;构建了诱导前即无停车信息下的停车选择模型,包括停车寻泊路径选择的logit概率模型和把不确定信息下转化为不完美信息的停车场选择方法。在此基础上对一个算例进行了模拟分析,得到了诱导前和诱导后各路段上的车辆数、各停车场的车位利用率以及排队等待入库的车辆数。     

Trip chaining as a barrier to the propensity to use public transport

Trip chaining is a growing phenomenon in travel and activity behaviour. Individuals increasingly seek out opportunities to minimise the amount of travel required as part of activity fulfilment, given the competing demands on time budgets and their valuation of travel time savings. This search for ways of fulfilling (more) activities with less travel input has produced a number of responses, one of which is trip chaining. A particularly important policy implication of trip chaining is the potential barrier it creates in attracting car users to switch to public transport. This paper seeks to improve our understanding of trip chaining as a barrier to public transport use. A series of discrete choice models are estimated to identify the role that socio-economic and demographic characteristics of households have on the propensity to undertake trip chains of varying degrees of simplicity/complexity that involve use of the car or public transport with an embedded commuting or non-commuting primary purpose. Multinomial logit, nested logit and random parameter logit models are developed and contrasted to establish the gains in relaxing the strict conditions of the multinomial logit model.     

Effective environmental marketing of green cars: A nested-logit approach

Using market data, we assess the effectiveness of an eco-marketing campaign on purchases of ‘green’ vehicles. The eco-marketing was designed as a quasi-experiment, having one region exposed to the marketing while the other region was the control. A two-level nested-logit model consistent with utility maximization reveals the campaign had short-term positive effects on green-car sales. Results also indicate green-car buyers come from highly educated communities. Age has a positive but non-linear effect on green car sales.     

Investigating the joint choice behavior of intercity transport mode and high‐speed rail cabin with a strategy map

This paper investigates the joint choice behavior of intercity transport modes and high‐speed rail cabin class within a two‐dimensional choice structure. Although numerous studies have been conducted on the mode choice behavior, little is known about the influence of cabin class on their intercity traveling choice. Hence, this study is conducted with a revealed preference survey to investigate the intercity traveling behavior for the western corridor of Taiwan. The results of nested logit model reveal that a cabin strategy has a more significant influence on cabin choice than on mode choice. Furthermore, this study proposes a new strategy map concept to assist transport operators in defining and implementing their pricing strategies. The results suggest that to capture a higher market share, high‐speed rail operators should choose an active price reduction strategy, while bus and rail operators are advised to implement a passive price increase strategy to raise unit revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

High‐speed rail cost recovery time based on an integer optimization model

With increasing gasoline prices, electric high‐speed rail (HSR) systems represent one means to mitigate overexposure to volatile prices. However, additional research is needed related to funding this infrastructure. In this paper, we develop a new integer optimization model to address this problem and use a hypothetical case study to demonstrate the approach. The objective of the approach is to minimize the time period in which the cost of HSR construction and operation can be recovered. This is an iterative process based on an integer optimization model, whose objective function is to determine the optimum recovery time (ORT), by setting the HSR ticket price and frequency. Embedded in the optimization model is a multinomial logit model for calculating the demand for HSR as a function of these decision variables, thus capturing the effects of level of service on market share. In particular, the optimization model accounts for the role of different types of subsidies toward HSR construction (one‐time subsidies at construction, annual subsidies, and subsidies depending on frequency). This method can also help determine whether an HSR system should be built or how much subsidy should be provided given a fixed expected cost recovery time. By integrating the logit model into the objective function evaluation, the effects of ticket price and service frequency on service demand can be directly captured. Copyright © 2014 John Wiley & Sons, Ltd.