Discrete mode-choice analysis of urban travel demand by the Analytic Hierarchy Process

This paper develops a new procedure for the problem of multimodal urban corridor travel demand estimation by using the Analytic Hierarchy Process (AHP). Certain conceptual and operational features of the AHP are common to the discrete choice theory-based modeling approach. Whereas the computational and data requirements of standard discrete choice models are immense, the proposed AHP approach deals efficiently with multidimensionality, nested demand structure and discrete travel decision making behavior. The paper concludes by summarizing the AHP-aided, step-by-step procedure for metropolitan travel demand (modal split) estimation.     

Analysis of Metropolitan Highway Capacity and the growth in vehicle miles of travel

A number of recent studies have examined the hypothesis of induced travel in an attempt to quantify the phenomenon (Hansen & Huang 1997; Noland, forthcoming). No study has yet attempted to adjust for potential simultaneity bias in the results. This study addresses this issue by the use of an instrumental variable (two stage least squares) approach. Metropolitan level data compiled by the Texas Transportation Institute for their annual congestion report is used in the analysis and urbanized land area is used as an instrument for lane miles of capacity. While this is not an ideal instrument, results still suggest a strong causal relationship but probably that most previous work has had an upward bias in the coefficient estimates. The effect of lane mile additions on VMT growth is forecast and found to account for about 15% of annual VMT growth with substantial variation between metropolitan areas. This effect appears to be closely correlated with percent growth in lane miles, suggesting that rapidly growing areas can attribute a greater share of their VMT growth to growth in lane miles.     

Revisiting the notion of induced traffic through a matched-pairs study

In investigating the question of the existence of "induced demand" in connection with highway expansion projects, Hansen et al. (1993) studied eighteen California state highway segments whose capacities had been improved in the early 1970s. For the present study, these segments were paired with control segments that matched the improved segments to unimproved ones with regard to facility type, region, approximate size, and initial volumes and congestion levels. Taking annual data for average daily traffic (ADT) and design-hour-traffic-to-capacity (DTC) ratios during the 21 years 1976–1996, three approaches were used to compare growth rates between the improved and unimproved segments: overall growth comparisons for the matched pairs, repeated measures analysis, and analysis of matched mean profiles. We found the growth rates between the two types of segments to be statistically and practically indistinguishable, suggesting that the capacity expansions, in and of themselves, had a negligible effect on traffic growth over the period studied. Reasons for the differences between these results and those of aggregate cross-sectional models finding a significant induced demand effect are discussed. Our analyses suggest that the aggregate models may overestimate induced traffic due to the attribution of at least a fraction of the observed traffic growth to "induced demand" rather than to some of the confounding factors which were not controlled for in such studies. At the same time, it is noted that the traffic induced by capacity expansion may in certain circumstances be larger than that observed in the present study, with the effect of new highway construction on traffic growth being a prime candidate for scrutiny in this regard. The results of this study nonetheless suggest that, for existing facilities, the size of the induced-traffic effect that can be attributed to capacity enhancements may be sufficiently small that its detection in a case-control study would be difficult, if not impossible, without a substantially larger sample size.     

Short‐term travel time forecasts for transport information system in hong kong

In this paper, a case study is carried out in Hong Kong for demonstration of the Transport Information System (TIS) prototype. A traffic flow simulator (TFS) is presented to forecast the short‐term travel times that can be served as a predicted travel time database for the TIS in Hong Kong. In the TFS, a stochastic deviation coefficient is incorporated to simulate the minute‐by‐minute fluctuation of traffic flows within the peak hour period. The purposes of the case study are: 1) to show the applicability of the TFS for larger‐scale road network; and 2) to illustrate the short‐term forecasting of path travel times in practice. The results of the case study show that the TFS can be applied to real network effectively. The predicted travel times are compared with the observed travel times on the selected paths for an OD pair. The results show that the observed path travel times fall in the 90% confidence interval of the predicted path travel times.     

Telecommuting and travel: state of the practice,state of the art

This paper provides an overview of the status of telecommuting in the United States, especially as it relates to changes in travel behavior. Regarding the state of the practice, the paper discusses some refinements to the definition of telecommuting that have developed through increased operational experience. It reports several policy statements involving telecommuting, and explores the appeal of telecommuting as a public policy instrument. It highlights some trends in the implementation of home-based and work center-based telecommuting, and suggests that visible public-sector involvement has been crucial to the increased activity in this area.In sketching the state of the art, the paper outlines some frequently-stated hypotheses on telecommuting and travel behavior, and summarizes current empirical findings relating to those hypotheses. Finally, it suggests a variety of topics suitable for further research. These include studying factors influencing the ultimate adoption levels of telecommuting; impacts on energy/air quality, mode choice, and location/urban form; interactions with other transportation demand management strategies; relationships to the traditional urban travel demand forecasting process; cost/benefit tradeoffs; and telecommuting centers.     

Temporal disaggregation of travel demand for high resolution emissions inventories

The commonly used photochemical air quality model, the Urban Airshed Model (UAM), requires emission estimates with grid-based, hourly resolution. In contrast, travel demand models, used to simulate the travel activity model inputs for the transportation-related emissions estimation, typically only provide traffic volumes for a specific travel period (e.g. the a.m. and p.m. peak periods). A few transportation agencies have developed procedures to allocate period-based travel demand data into hourly emission inventories for regional grid cells. Because there was no theoretical framework for disaggregating period-based volumes to hourly volumes, application of these procedures frequently relied upon a single hypothetical hourly distribution of travel volumes. This study presents a new theoretical modeling framework that integrates traffic count data and travel demand model link volume estimates to derive intra-period hourly volume estimates by trip purpose. We propose a new interpretation of the model coefficients and define hourly allocation factors by trip purpose. These allocation factors can be used to disaggregate the travel demand model ‘period-based’ simulation volumes into hourly resolution, thereby improving grid-based, hourly emission estimates in the UAM.     

Micro-simulation of daily activity-travel patterns for travel demand forecasting

The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns. This revised version was published online in June 2006 with corrections to the Cover Date.     

城市绿色出行内涵和评价指标体系框架研究

绿色出行发展的根本目的是为了实现城市交通可持续发展,实现出行安全、畅通、高效、舒适、环保、节能,从而实现社会、经济、交通和环境的协调发展。本文通过对绿色出行的概念、内涵、特征和实现途径等相关理论进行解读,确定绿色出行系统的主要构成;采用计划行为理论、交通需求管理理论等多视角,对影响和制约城市绿色出行发展的关键因素进行分析和识别,并研究提出围绕保障能力、基础设施、运输装备、运营服务等方面的绿色出行评价指标体系框架。     

Investigation of the use of smartphone applications for trip planning and travel outcomes

This paper explores the use of smartphone applications for trip planning and travel outcomes using data derived from a survey conducted in Halifax, Nova Scotia, in 2015. The study provides empirical evidence of relationships of smartphone use for trip planning (e.g. departure time, destination, mode choice, coordinating trips and performing tasks online) and resulting travel outcomes (e.g. vehicle kilometers traveled, social gathering, new place visits, and group trips) and associated factors. Several sets of factors such as socio-economic characteristics and travel characteristics are tested and interpreted. Results suggest that smartphone applications mostly influence younger individuals’ trip planning decisions. Transit pass owners are the frequent users of smartphone applications for trip planning. Findings suggest that transit pass owners commonly use smartphone applications for deciding departure times and mode choices. The study also identifies the limited impact of smartphone application use on reducing travel outcomes, such as vehicle kilometers traveled. The highest impact is in visiting new places (a 48.8% increase). The study essentially offers an original in-depth understanding of how smartphone applications are affecting everyday travel.     

Applying behavioural theories to studying the influence of climate change on young people’s future travel intentions

Transport policy in the UK is seeking to promote the development of low carbon transport technology and to encourage people to choose to use low carbon travel options. This paper draws on existing behavioural theories to study young people’s travel behaviour intentions and the influence on these from their knowledge of, and willingness to act on, climate change. The study involved a series of focus groups with young people aged 11-18 years, where attitudes to transport modes, attitudes towards climate change and travel behaviour intentions were discussed. Knowledge and values are established as the key determinants of young people’s attitudes and behaviour intentions towards transport in the context of climate change. More specifically it is established that young people’s values emphasise speed and freedom and that it is important to young people that the mode of transport they choose is reflective of the image they want to portray.     

A conceptual and methdological framework for the generation of activity-travel patterns

This paper develops a conceptual framework for the generation of activity and travel patterns in the context of more general structures and presents an integrated model system as a step toward development of an improved travel demand forecasting model system. We propose a two-stage structure to model activity and travel behavior. The first stage, the stop generation and stop/auto allocation models, consists of the choices for the number of household maintenance stops and the allocation of stops and autos to household members. The second stage, the tour formation model, includes the choices for the number of tours and the assignment of stops to tours for each individual, conditional on the choices in the first stage. Empirical results demonstrate that individual and household socio-demographics are important factors affecting the first stage choices, the generation of maintenance stops and the allocation of stops and autos among household members, and the second stage choices, the number of tours and the assignment of stops to tours. This revised version was published online in June 2006 with corrections to the Cover Date.     

共享单车停放需求影响因素分析和预测研究

为了解决城市共享单车的乱停乱放问题,本文基于北京市的共享单车出行大数据,提出了共享单车停放需求预测的多项Logit模型。首先分析了单车停放需求的影响因素,然后选取了时间、空间及天气方面的12个因素为自变量,通过Wald检验分析了这些因素与停放需求的相关性和显著性,基于多项Logit模型建立了共享单车的停放需求预测模型。结果表明:工作日、时段、商业区、所临道路类型、临近轨交站、高温、下雨、以及风力等级与共享单车停放需求显著相关;构建的预测模型总体预测准确率为77.5%,其中对出现频率最高的低停放需求预测准确率高达86.49%。