A study of tour-based mode choice based on a Support Vector Machine classifier

A new approach in recognizing travel mode choice patterns is proposed, based on the Support Vector Machine classification technique. The tour-based travel demand dataset that is analysed is for New York State, derived from the 2009 U.S. National Household Travel Survey. The main features characterizing each tour are the means used, travel-related variables and socioeconomic aspects. Results obtained demonstrate the ability to predict to some extent, in real settings where car use dominates, which tours are likely to be made by public transport or non-motorized means. Moreover, the flexibility of the technique allows assessing the predictive power of each feature according to the combination of travel means used in different tours. Potential applications range from activity-based travel choice simulators to search engines supporting personalized travel planners – in general, whenever ‘best guesses’ on mode choice patterns have to be made quickly on large amounts of data prejudicing the possibility of setting up a statistical model.     

城市交通方式的出行成本量化方法研究

本文通过对步行、自行车、摩托车、公共汽车、小汽车、出租汽车、轻轨、地铁等各种交通方式在使用成本、基础设施成本、出行时间成本以及外部成本等方面进行量化分析,确定了城市出行成本的计算方法,从而为各个城市优化交通结构提供依据。     

关于减少公交出行换乘次数的研究

本文对公交出行单元的划分、公交出行单元的联接和公交出行换乘次数小于等于1的充分条件等公交线网设计的基础问题进行了比较系统的讨论,并以棋盘型道路和放射型道路为例,说明了上述充分条件的应用,对当前公交线网改造及后续深化研究具有较好的参考作用。     

Forecasting Travel Demand with Alternatively Structured Models of Trip Frequency

Abstract

This paper develops alternatively structured trip frequency/generation models, and investigates their forecast performance. The first model presented is the simple linear model with a discussion of its theoretical shortcomings. Models that address, in a progressive fashion, the underlying shortcomings of the linear model are then presented. These models are namely the truncated normal model, the Poisson model, the negative binomial model, and an ordered logit model. The modeling unit employed in the study is the individual. The models are assessed by how closely they are able to replicate trips produced by each individual in the dataset, and by each traffic zone. This assessment of performance in prediction is conducted on an estimation dataset collected in the Toronto Region in 1986, and on an independent dataset collected in the same geographic region, 10 years later, in 1996. The results show that, notwithstanding the simplicity of the simple linear model and its lack of an explicit underlying travel behavioral theory, it predicts travel in the base and forecast years with less error compared to any of the more complex models.     

Travel time variability and airport accessibility

We analyze the cost of access travel time variability for air travelers. Reliable access to airports is important since the cost of missing a flight is likely to be high. First, the determinants of the preferred arrival times at airports are analyzed. Second, the willingness to pay (WTP) for reductions in access travel time, early and late arrival time at the airport, and the probability to miss a flight are estimated, using a stated choice experiment. The results indicate that the WTPs are relatively high. Third, a model is developed to calculate the cost of variable travel times for representative air travelers going by car, taking into account travel time cost, scheduling cost and the cost of missing a flight using empirical travel time data. In this model, the value of reliability for air travelers is derived taking “anticipating departure time choice” into account, meaning that travelers determine their departure time from home optimally. Results of the numerical exercise show that the cost of access travel time variability for business travelers are between 0% and 30% of total access travel cost, and for non-business travelers between 0% and 25%. These numbers depend strongly on the time of the day.     

Modelling the effects of road pricing on traffic using ERP traffic data

Singapore’s Electronic Road Pricing (ERP) system involves time-variable charges which are intended to spread the morning traffic peak. The charges are revised every three months and thus induce regular motorists to re-think their travel decisions. ERP traffic data, captured by the system, provides a valuable source of information for studying motorists’ travel behaviour. This paper proposes a new modelling methodology for using these data to forecast short-term impacts of rate adjustment on peak period traffic volumes. Separate models are developed for different categories of vehicles which are segmented according to their demand elasticity with respect to road pricing. A method is proposed for estimating the maximum likelihood value of preferred arrival time (PAT) for each vehicle’s arrivals at a particular ERP gantry under different charging conditions. Iterative procedures are used in both model calibration and application. The proposed approach was tested using traffic datasets recorded in 2003 at a gantry located on Singapore’s Central Expressway (CTE). The model calibration and validation show satisfactory results.