Modeling the evolutions of day‐to‐day route choice and year‐to‐year ATIS adoption with stochastic user equilibrium

Suppose that in an urban transportation network there is a specific advanced traveler information system (ATIS) which acts for reducing the drivers' travel time uncertainty through provision of pre‐trip route information. Because of the imperfect information provided, some travelers are not in compliance with the ATIS advice although equipped with the device. We thus divide all travelers into three groups, one group unequipped with ATIS, another group equipped and in compliance with ATIS advice and the third group equipped but without compliance with the advice. Each traveler makes route choice in a logit‐based manner and a stochastic user equilibrium with multiple user classes is reached for every day. In this paper, we propose a model to investigate the evolutions of daily path travel time, daily ATIS compliance rate and yearly ATIS adoption, in which the equilibrium for every day's route choice is kept. The stability of the evolution model is initially analyzed. Numerical results obtained from a test network are presented for demonstrating the model's ability in depicting the day‐to‐day and year‐to‐year evolutions.     

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990–2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.     

Cognitive cost in route choice with real-time information: An exploratory analysis

Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.     

Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment

In this paper, we report the results of a stated choice experiment, which was conducted to examine truck drivers?? route choice behavior. Of particular interest are the questions (i) what is the relative importance of road accessibility considerations via-a-vis traditional factors influencing route choice behavior, (ii) what are the influences of particular personal and situational variables on the evaluation of route attributes, (iii) how sensitive are truck drivers for possible pricing policies, and (iv) is there a difference in impact if environmental concerns are framed as a bonus or as a pricing instrument. The main findings indicate that road accessibility characteristics have a substantial impact on route preferences which is of the same order of magnitude as variation in travel times. This suggests that provision of adequate travel information in itself can be an effective instrument to prevent negative externalities of good transport associated with shortest routes. Furthermore, the results indicate that truck drivers/route planners when choosing a route are relatively sensitive to road pricing schemes and rather insensitive to environmental bonuses.     

Value of travel time: An activity‐based analysis with combined RP and SP data

Under certain assumptions, values of travel time (VOT) can be divided into two parts: one associated with travel (i.e., the value of reducing travel time disutility) and the other associated with activities (the shadow price of time). The empirical results of combining RP and SP data showed that the VOT of travel was greater than that of activity. The results also showed that SP experiments that did not take or only took part of activities into consideration would underestimate VOT.     

The future for in‐vehicle information systems: The technology and its impacts

In‐vehicle information has an important social role to play in improving the efficiency and safety of travel by all modes. In this review, three generations of system are identified. The first generation consists of simple in‐vehicle units relying heavily on external data. The second generation has more sophisticated in‐vehicle units with colour TFT screens and DVD players for maps and entertainment. The third generation again makes use of external data, using the mobile phone network to download map sections and other data as and when required, thereby obviating the need for beacons and map CDs. For locationing, GPS (and/or Galileo, the European version of GPS) remains the favoured technology. Portable devices offering multi‐modal information could improve inter‐modal transport efficiency.     

The evaluation of high occupancy vehicle lanes on sun yat‐sen freeway in Taiwan

This study proposes a methodological framework to incorporate latent factors, including direct and indirect perceptions, as the explanatory variables in a discrete choice models using revealed preference and stated preference data sets. The methodology requires the estimation of a model system comprising of a discrete choice model and the structural and measurement equations of a latent variable model. The application involves the evaluation of responses to the new high occupancy vehicle (HOV) lanes on the Sun Yat‐Sen Freeway in Taiwan. The results obtained from this study provide valuable insights into the planning and assessment of HOV lanes.     

The effect of the price of gasoline on the urban economy: From route choice to general equilibrium

RELU-TRAN2, a spatial computable general equilibrium (CGE) model of the Chicago MSA is used to understand how gasoline use, car-VMT, on-the-road fuel intensity, trips and location patterns, housing, labor and product markets respond to a gas price increase. We find a long-run elasticity of gasoline demand (with congestion endogenous) of ?0.081, keeping constant car prices and the TFI (technological fuel intensity) of car types but allowing consumers to choose from car types. 43% of this long run elasticity is from switching to transit; 15% from trip, car-type and location choice; 38% from price, wage and rent equilibration, and 4% from building stock changes. 79% of the long run elasticity is from changes in car-VMT (the extensive margin) and 21% from savings in gasoline per mile (the intensive margin); with 83% of this intensive margin from changes in congestion and 17% from the substitution in favor of lower TFI. An exogenous trend-line improvement of the TFI of the car-types available for choice raises the long-run response to a percent increase in the gas price from ?0.081 to ?0.251. Thus, only 1/3 of the long-run response to the gas price stems from consumer choices and 2/3 from progress in fuel intensity. From 2000 to 2007, real gas prices rose 53.7%, the average car fuel intensity improved 2.7% and car prices fell 20%. The model predicts that from these changes alone, keeping constant population, income, etc. aggregate gasoline use in this period would have fallen by 5.2%.     

A study on route choice preferences for commuter and non-commuter bicyclists: a case study of Kharagpur and Asansol,India

Transportation - The bicycle is one of the most affordable and flexible means of transportation in a developing country such as India. Despite being an important mode of transportation, a sharp...     

Calibrating travel time thresholds with cluster analysis and AFC data for passenger reasonable route generation on an urban rail transit network

Transportation - Estimating the route choice patterns for transit passengers is important to improve service reliability. The size and composition of a route choice set affects the choice model...     

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

Transportation - In the face of a continuous increase in the number of tourists in the Alps, the associated traffic volumes, and the resulting negative externalities, there is an urgent need to...     

Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

Studies that link human behaviour to the influence of weather have historically been conducted in such fields as tourism, marketing and leisure. In most studies that jointly examine weather and the mode of transport, only open-air transportation has been considered (for example, bicycle, motorcycle or walking). This focus, together with the habitual use of data collected with automatic devices and a lack of studies that analyse this issue using stated preference data, are the main reasons motivating this paper. This paper aims to analyse the influence of weather and the density of traffic on the choice of transport mode. A case study is conducted in an access/egress corridor located in the city of Barcelona (Spain). Two data sources were used: revealed preference and stated preference data. Modelling techniques using mixed data enabled the stronger features from both data sources to be captured. Finally, we discuss how the selection of different alternative specific constants in models estimated using mixed data could generate unrealistic forecasting results if environmental changes are expected in the actual market.     

Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips

This paper analyzes transportation mode choice for short home-based trips using a 1999 activity survey from the Puget Sound region of Washington State, U.S.A. Short trips are defined as those within the 95th percentile walking distance in the data, here 1.40 miles (2.25 km). The mean walking distance was 0.4 miles (0.6 km). The mode distribution was automobile (75%), walk (23%), bicycle (1%), and bus (1%). Walk and bicycle are found less likely as the individual’s age increases. People are more likely to drive if they can or are accustomed to. People in multi-person families are less likely to walk or use bus, especially families with children. An environment that attracts people’s interest and provides activity opportunities encourages people to walk on short trips. Influencing people’s choice of transport mode on short trips should be an important part of efforts encouraging the use of non-automobile alternatives.

The multi‐actor,multi‐criteria analysis methodology (MAMCA) for the evaluation of transport projects: Theory and practice

In this paper the multi‐actor multi‐criteria analysis (MAMCA) method to evaluate transport projects is presented. This evaluation method specifically focuses on the inclusion of qualitative as well as quantitative criteria with their relative importance, defined by the multiple stakeholders, into one comprehensive evaluation process in order to facilitate the decision making process by the different stakeholders. The MAMCA methodology is introduced by an overview of other evaluation methods for transport projects in the past and is illustrated by means of two practical cases. The introduction will lead us to the theoretical conception of the MAMCA method where we draw the attention to the proven usefulness of the MAMCA for the evaluation of transport projects and the inclusion of different kinds of stakeholders, individuals as well as groups, into the evaluation process.     

Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour

There is growing interest in the use of models that recognise the role of individuals’ attitudes and perceptions in choice behaviour. Rather than relying on simple linear approaches or a potentially bias-inducing deterministic approach based on incorporating stated attitudinal indicators directly in the choice model, researchers have recently recognised the latent nature of attitudes. The uptake of such latent attitude models in applied work has however been slow, while a number of overly simplistic assumptions are also commonly made. In this article, we present an application of jointly estimated attitudinal and choice models to a real-world transport study, looking at the role of latent attitudes in a rail travel context. Our results show the impact that concern with privacy, liberty and security, and distrust of business, technology and authority have on the desire for rail travel in the face of increased security measures, as well as for universal security checks. Alongside demonstrating the applicability of the model in applied work, we also address a number of theoretical issues. We first show the equivalence of two different normalisations discussed in the literature. Unlike many other latent attitude studies, we explicitly recognise the repeated choice nature of the data. Finally, the main methodological contribution comes in replacing the typically used continuous model for attitudinal response by an ordered logit structure which more correctly accounts for the ordinal nature of the indicators.     

Decisions on truck parking place and time on expressways: an analysis using digital tachograph data

With the objective of deriving useful insights into measures against traffic congestion at service areas (SAs) and parking areas (PAs) on expressways and ensuring efficient use of SAs/PAs, this study investigated the decisions on where a truck is parked (i.e., choice of an SA or a PA), how long it is parked (i.e., parking time), and their influential factors. To this end, this study used the trajectory data of 1600 trucks recorded in 6-min intervals by in-vehicle digital tachographs on the Sanyo and Chugoku Expressways in Japan from October 2013 to March 2014. First, the aspect of repeated choice of each truck (i.e., habitual behavior) toward a specific SA/PA was clarified. Next, a multilevel discrete–continuous model (Type II Tobit model) was developed to reveal the factors affecting the above decisions. The modeling results confirmed the existence of habitual behavior and showed that trucks were more likely to be parked a longer time at an SA/PA when it is closer to the destination. It appears that truck drivers may adjust their time at the SA/PA close to the destination to comply with the arrival time, which is often predetermined by the owner of the transported goods. Furthermore, the availability of restaurants and shops, and the number of parking spaces available for trucks and trailers are important determinants of parking time, whereas the existence of a convenience store is important to the choice of the SA/PA. Parking experience has an extremely strong positive effect on the parking choice and use. Moreover, increasing the number of parking lots may induce its longer use.

     

Built environment,travel behavior,and residential self-selection: a study based on panel data from Beijing,China

The influence of the built environment on travel behavior has been the subject of considerable research attention in recent decades. Scholars have debated the role of residential self-selection in explaining the associations between the built environment and travel behavior. The purpose of this study is to make a contribution to the literature by adopting the cross-lagged panel modeling approach to analyze a panel data, which scholars have recommended as the ideal design for studying the influence of the built environment on travel behavior accounting for the residential self-selection. To that objective, we collected activity-travel diary data from a sample of 229 households in Beijing before and after they moved from one residential location to another. We developed a two-wave structural equation model linking the residential built environment to travel behavior and taking into consideration travel-related attitudes before and after residential change. The modeling results show that individuals’ travel attitudes may change after a home relocation. We found no evidence of residential self-selection, but significant influence of the built environment on travel preference. Nevertheless, the direct influence of travel preference on travel behavior seems to be stronger than that of the built environment. As one of the very few studies to use panel data, this research presents new insights into the relationship between the built environment and travel behavior and the role of residential self-selection.

     

Long-term impact of the Shinkansen on rail and air demand: analysis with data from Northeast Japan

This paper analyses how the high-speed rail construction in Northeast Japan (Tohoku) has affected total demand and interregional travel patterns. We use annual interregional passenger data from 1989 to 2012 and apply regression analysis with the demand between Tokyo and the Tohoku prefectures as the dependent variable. We distinguish particularly between the ‘Full-’ and the ‘Mini-’ Shinkansen, where the latter are branch services running with reduced speed. We find that the ‘Full-Shinkansen’ quickly increases rail and total public transport trips and generates additional rail demand year on year. The ‘Mini-Shinkansen’ impacts are less pronounced. Furthermore, our analysis shows that the Shinkansen has shifted some demand from air to rail once it started operation and increased rail share gradually. We therefore suggest that predictions of demand impacts should carefully distinguish immediate from gradual impacts. We also discuss differences in regional demand in that not all prefectures have gained equally from Shinkansen construction.     

Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes

Transportation - Activity sequencing is a crucial component of disaggregate modeling approaches. This paper presents a methodology to analyse and predict activity sequence patterns for persons...