自动化公路货物运输量统计方法

为了提高中国公路货运统计的真实性和统计效率,利用自动化交调、轴载检测、高速公路计费、联网联控货车以及国家道路货运量专项调查等数据,提出2种自动化公路货运量统计方法.2种方法均采用浮动检测数据与固定检测数据融合的原理,使用浮动车数据获得运输特征,分别使用高速计费数据及自动化交调数据获得运输规模.通过设备检测数据与人工调查...     

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公路超限运输危害巨大,为有效治超,深入分析超限车辆的出行选择行为.超限车辆为实现运输效益最大化,将对其超限程度、出行线路等做出一系列出行选择,不同的出行选择决策决定超限车辆的出行成本和出行收益大小.在公路超限车辆出行选择决策行为和出行效用分析的基础上,建立超限车辆出行超限程度与出行线路这种存在层次关系决策行为的巢式Logit逻辑决策模型.从概率角度用定量方法分析超限车辆出行个体的出行选择行为,为公路超限运输的治理工作提供科学指导,同时也将为超限车辆补偿费率制定提供理论支持.     

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在分析通勤者出行时间与出行方式选择特征的基础上,建立出行时间—出行方式与出行方式—出行时间两个方向的Nested Logit模型.利用北京市第三次居民出行调查数据,借助BIOGEME软件对两个模型进行估计.首先通过包容系数对Nested Logit模型的结构关系进行辨识,结果显示,出行时间—出行方式选择模型比出行方式—出行时间选择模型合理,表明通勤者在选择出行时间后,在其约束下考虑合适的出行方式;在此基础上,对出行时间—出行方式选择模型的参数进行标定并分析标定结果,进一步揭示通勤者出行时间与出行方式的选择特征,从而为制定高峰时期相关管理政策提供理论依据.     

Testing port choice models using physical and monetary data: a comparative case study for the Spanish container trades

ABSTRACT

Transport costs are useful explanatory variables in port choice research. Nevertheless, the availability of such information usually poses a problem. Thus, the formulation of an alternative approach, to be used as a proxy of these variables, would be desirable. The objective of this study is to improve the analysis of container port choice using logit models by adopting physical non-monetary indicators based on maritime distance and ship size. The statistical tests of logit models on port choice using these new variables are compared with the result of using cost variables for the same data set of choice positions. The statistical outcome is good and allows us to present this new approach as a contribution to the literature on port choice modeling.     

基于logit预测模型分析高铁建设对交通的影响

近年来,中国高速铁路建设发展突飞猛进.同时,随着生活水平的不断提高,中国人民对高速铁路的需求也在日益增加.以此为背景,参考已有研究选取合适的指标构建logit模型预测市民出行方式,分析高铁开通对高速公路的车辆通行压力以及对人们远途出行方式选择的影响,最后融合损失厌恶函数与攀比效应改进模型,研究预测发展不同城市的高铁客运...     

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00 am to 6 am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70 km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7 pm to 12 pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70 km/h speed limit and double lane line road marking.     

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.     

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.     

Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models

The rapid and continuing changes in travel and mobility needs in India over the last decade necessitates the development and use of dynamic models for travel demand forecasting rather than cross-sectional models. In this context, this paper investigates mode choice dynamics among workers in Chennai city, India over a period of five years (1999–2004). Dynamics in mode choice is captured at four levels: exogenous variable change, state-dependence, changes in users’ sensitivity to attributes, and unobserved error terms. The results show that the dynamic models provide a substantial improvement (of over 500 log-likelihood points and ρ² increases from 44% to 68%) over the cross-sectional model. The performance was compared using two illustrative policy scenarios with important methodological and practical implications. The results indicate that cross-sectional models tend to provide inflated estimates of potential improvement measures. Improving the Level of Service (LOS) alone will not produce the anticipated benefits to transit agencies, as it fails to overcome the persistent inertia captured in the state-dependence factors. The results and models have important applications in the context of growing motorization and congestion management in developing countries.

Variation in the value of travel time savings and its impact on the benefits of managed lanes

This paper examines the variation in the value of travel time savings (VTTS) for travelers with a managed lane (ML) option when taking an ordinary trip versus a trip that is unusual in some way. VTTS estimates vary substantially depending on the urgency of the trip made. At the low end, the mean VTTS for a traveler who wants to make extra stops and still arrive on time is approximately 10% higher than that for an ordinary trip. At the high end, a traveler running late for an appointment shows a mean VTTS that is approximately 300% higher than that for an ordinary trip. These estimates vary widely over the population of travelers. In light of these variations, the value of an uncongested travel alternative (such as MLs) is examined and found to be greatly undervalued if using typical VTTS estimates.