Measuring Impacts of New Highways Capacity – A Discussion of Potential Survey Methods

The paper reviews survey methods that might be used to detect the various impacts of new highway capacity (changes in flow and network travel times; behavioural responses such as rerouting, change in departure times, change of mode, redistribution and change in trip frequency; and changes in land use). The review was conducted in the context of a study for TRRL which sought to establish the feasibility of measuring responses to new highway capacity. The paper considers, in turn, surveys of traffic flow, public transport usage and network travel times, methods of estimating origin-destination matrices and a variety of questionnaire and interview techniques which might be used to collect individual travel data (roadside interviews; stopline surveys; household interviews; trip-end interviews; self completion questionnaires; retrospective, prospective and stated preference questions; panel surveys and indepth interviews). There is also a brief discussion of methods to determine bight movements and land use effects. The paper should not be regarded as a source of detailed information about the various types of survey but rather as a review of their comparative strengths and weaknesses in the given context

New ITS applications for metropolitan areas based on Floating Car Data

The paper describes a couple of FCD based vehicular traffic applications and services. This new method is especially beneficial for regions with a poor traffic monitoring infrastructure because the necessary monetary effort to establish such a system is very small in comparison to conventional systems and it is flexible and easily adaptable to other regions. Particularly, emerging markets like China with a fast-changing road network and a high penetration of lat-est information technologies on one side but with serious foreseeable traffic related problems on the other side can surely profit from this approach. The new data collection and analysing methods result in better performance of the services enhance the scope of the services and hopefully enlarge user acceptance. All of the proposed solutions are prototypes and not all of them have been extensively tested up to now. Certainly, specific data processing methods need further research, some refinements and calibrations. Additionally, some applications still suffer from insufficient data penetration. Nevertheless, the approach is very general and it is very likely that FCD availability will sharply increase in near future and will enhance the quality of services

Assessing the Value of Time Travel Savings – A Feasibility Study on Humberside.

It is expected that the opening of the Humber Bridge will cause major changes to travel patterns around Humberside; given the level of tolls as currently stated, many travellers will face decisions involving a trade-off between travel time, money outlay on tolls or fares and money outlay on private vehicle running costs; this either in the context of destination choice, mode choice or route choice. This report sets out the conclusions of a preliminary study of the feasibility of inferring values of travel time savings from observations made on the outcomes of these decisions. Methods based on aggregate data of destination choice are found t o be inefficient; a disaggregate mode choice study i s recommended, subject to caveats on sample size

Exploring the potential for cross-nesting structures in airport-choice analysis: A case-study of the Greater London area

The analysis of air-passengers’ choices of departure airport in multi-airport regions is a crucial component of transportation planning in many large metropolitan areas, and has been the topic of an increasing number of studies over recent years. In this paper, we advance the state of the art of modelling in this area of research by making use of a Cross-Nested Logit (CNL) structure that allows for the joint representation of inter-alternative correlation along the three choice dimensions of airport, airline and access-mode. The analysis uses data collected in the Greater London area, which arguably has the highest levels of inter-airport competition of any multi-airport region; the authors of this paper are not aware of any previous effort to jointly analyse the choice of airport, airline and access-mode in this area. The results of the analysis reveal significant influences on passenger behaviour by access-time, access-cost, flight-frequency and flight-time. A structural comparison of the different models shows that the cross-nested structure offers significant improvements over simple Nested Logit (NL) models, which in turn outperform the Multinomial Logit (MNL) model used as the base model

Measuring Marginal Congestion Costs of Urban Transportation: Do Networks Matter?

In determining the marginal cost of congestion, economists have traditionally relied upon directly measuring traffic congestion on network links, disregarding any “network effects,” since the latter are difficult to estimate. While for simple networks the comparison can be done within a theoretical framework, it is important to know whether such network effects in real large-scale networks are quantitatively significant. In this paper we use a strategic transportation planning model (START) to compare marginal congestion costs computed link-by-link with measures taking into account network effects. We find that while in aggregate network effects are not significant, congestion measured on a single link is a poor predictor of total congestion costs imposed by travel on that link. Also, we analyze the congestion proliferation effect on the network to see how congestion is distributed within an urban area.marginal congestion costs, congestion pricing, urban networks

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Robustness of optimal inter-city railway network structure in Japan against alternative population distributions

It takes long time and huge amount of money to construct inter-city railway network. Careful demand forecasting and rational service planning are therefore required. However, long ranged demand forecasting is always facing to unintended change of regional population or change of the service level of competing transportation modes such as airline and inter-city express bus. Those changes sometimes resulted in severe decrease of demand for the constructed railway lines and discussion of abolishment of train service occurs. In order to avoid such tragedy, we want to build a robust network plan not vulnerable for the changes in forecasting conditions. This paper discusses the robustness of optimal inter-city railway network structure in Japan against alternative population distributions. Genetic Algorithm is applied to find best mixture of maximum operation speed category and number of daily train service for each link, which maximize the total consumer surplus of inter-city railway passengers. Consumer surplus is assessed by a gravity demand model considering service level along several routes for each OD pair. Travel time calculated by allocated link speed category, allocated train frequency, and estimated fare regressed by travel speed, will be summarized as route service level via ML route choice model parameters. In the GA, we consider a chromosome consists of two parts; speed category of 275 links and relative operation distance of trains in those links. Besides the real distribution of population in 197 Japanese local areas in the year of 1995, we set four other hypothetic population distributions; two of them concentrate in megalopolises like Tokyo, others disperse along geographically remote areas. We first obtain network structures optimized by the GA for each population setting. Speed category allocation will be compared for the five network plans. Secondly, we calculate total consumer surplus of each network plan under the different population settings and discuss the vulnerability of those plans. Thirdly, we optimize train operation plans for different population settings under the given speed category arrangements. The results shows that spatial arrangement of high speed railway service in 1995 keeps optimality for wide range of population settings, if we adjust number of trains according to alternative population distribution.