Changes in perceived travel cost and time for the work trip during a period of increasing gasoline costs

This study was designed to examine the relationship between actual and perceived values of cost and time for the work trip and to examine how perceptions have changed over a period of dramatically increased travel costs. Variations in the relationship between perceived and actual values were examined as a function of situational and attitudinal variables. Two telephone surveys were conducted one year apart (Fall 1978 and Fall 1979). On the next working day following a survey, a research assistant recreated the respondent's work trip, recorded time values and used distance measures, car type information and parking costs to compute travel cost. The first survey revealed that most auto users were unable to articulate dollars-and-cents driving costs for the work trip, but auto users in the second survey were able to provide fairly accurate cost estimates. Dramatic changes in fuel prices between surveys is probably the main reason for the change in driving cost awareness. Auto users were also asked to rate relative costs of driving a car compared to using the bus for the work trip. These ratings showed that auto users tended to underestimate driving costs relative to bus costs, but this tendency decreased from the first to the second time period. Commuters in all modal groups at both time periods tended to overestimate travel times. Perception of travel time varied as a function of mode, perceived comfort (for car users), and perceived convenience and number of transfers (for bus users).To whom correspondence should be addressed.     

Effects on driving behavior of congestion information and of scale of in-vehicle navigation systems

The purpose of this research was to compare the usage of a traditional paper map and electronic route maps during driving, and to consider the effects of congestion information and map scale sizes on driving performance, workload and subjective feelings. Experiments were conducted in desktop virtual driving environments with a 17-in. color monitor simulating driving environments and a 14-in. color monitor showing different kinds of navigation systems. A total of 20 undergraduate students of National Tsing Hua University were paid to participate in the Experiment I. The criteria for driving performance were trip duration, driving speed and number of navigation errors. Heart rate was measured as an index for workload. The 5-point Likert-type questionnaire was used to reflect the perceived nervousness, fatigue and task difficulty. Results indicated that the performance difference between a paper map and electronic route maps depended on the design characteristics of electronic route maps. Comparisons among four electronic maps revealed two significant main effects of congestion information and scale sizes on trip duration. Besides, it seemed that the availability of congestion information was useful for reducing navigation errors. Neither statistically significant main effects nor interaction was found on subjective feelings and driving speed. Another 18 subjects were used in the Experiment II to determine the optimum map scale size. Finally, the implications of the findings may provide suggestions on designing safer and more efficient in-vehicle navigation systems.     

Bi-level GA and GIS for Multi-objective TSP Route Planning

Abstract

Route planning is usually carried out to achieve a single objective such as to minimize transport cost, distance traveled or travel time. This article explores an approach to multi-objective route planning using a genetic algorithm (GA) and geographical information system (GIS) approach. The method is applied to the case of a tourist sight-seeing itinerary, where a route is planned by a tour operator to cover a set of places of interest within a given area. The route planning takes into account four criteria including travel time, vehicle operating cost, safety and surrounding scenic view quality. The multi-objective route planning in this paper can be viewed as an extension of the traditional traveling salesman problem (TSP) since a tourist needs to pass through a number of sight points. The four criteria are quantified using the spatial analytic functions of GIS and a generalized cost for each link is calculated. As different criteria play different roles in the route selection process, and the best order of the multiple points needs to be determined, a bi-level GA has been devised. The upper level aims to determine the weights of each criterion, while the lower level attempts to determine the best order of the sights to be visited based on the new generalized cost that is derived from the weights at the upper level. Both levels collaborate during the iterations and the route with the minimal generalized cost is thus determined. The above sight-seeing route planning methodology has been examined in a region within the central area of Singapore covering 19 places of interest.     

Dynamics of commuting decision behaviour under advanced traveller information systems

This paper addresses departure time and route switching decisions made by commuters in response to Advanced Traveller Information Systems (ATIS). It is based on the data collected from an experiment using a dynamic interactive travel simulator for laboratory studies of user responses under real-time information. The experiment involves actual commuters who simultaneously interact with each other within a simulated traffic corridor that consists of alternative travel facilities with differing characteristics. These commuters can determine their departure time and route at the origin and their path en-route at various decision nodes along their trip. A multinomial probit model framework is used to capture the serial correlation arising from repeated decisions made by the same respondent. The resulting behavioural model estimates support the notion that commuters' route switching decisions are predicated on the expectation of an improvement in trip time that exceeds a certain threshold (indifference band), which varies systematically with the remaining trip time to the destination, subject to a minimum absolute improvement (about 1 min).     

Driving Forces behind the Growth of Per‐capita Car Driving Distance in the UK, 1970–2000

Abstract

Although per‐capita car trip distance (measured in passenger‐km) and car driving distance (measured in vehicle‐km) in the UK have kept increasing, their growth rates slowed considerably in the 1990s when compared with the 1970s and 1980s. The paper investigates the main driving forces behind the changes in car trip and car driving distances, and it examines the determining factors for the slow down of growth in the 1990s on the basis of the analysis of data from the National Travel Survey (1975/76, 1989/91, 1992/94, 1995/97 and 1999/2001). In particular, it emphasizes the significance of changes in car ownership levels as a key driving force and attempts to separate this ‘car ownership effect’ from other effects. The log‐mean Divisia index decomposition method is applied to measure the relative contribution of each effect. Separate analyses are undertaken according to trip purpose. Other underlying causes, such as changes in fuel price and road capacity, are also examined.     

Stated preferences for investigating commuters' diversion propensity

A reasonable response to increasing traffic congestion may come from the rapidly developing traveler information systems. Such systems may be successful if they effectively influence drivers' enroute decisions; in this regard, a critical factor may be commuters' willingness to divert from their regular route in response to information about traffic congestion. This study evaluates the effects of real-time traffic information along with driver attributes, roadway characteristics and situational factors on drivers' willingness to divert.The empirical portion of this study is based on a survey of downtown Chicago automobile commuters. The stated preference approach was used to study commuters' diversion propensity. Drivers expressed a higher willingness to divert if expected delays on their usual route increased, if the congestion was incident-induced as opposed to recurring, if delay information was received from radio traffic reports compared with observing congestion, and if trip direction was home-to-work rather than work-to-home. Respondents were less willing to divert if their alternate route was unfamiliar, unsafe or had several traffic stops. Socioeconomic characteristics were also significant in predicting willingness to divert.     

Preliminary analysis on dynamic route choice behavior: Using probe‐vehicle data

Dynamic traffic assignment models have been attracting increasing attention with the progress of traffic management policies based on information technology. These dynamic estimation tools, however, just apply static route choice models either at only origin node or at every arrival node. This paper aims at providing some knowledge on drivers' dynamic route choice behavior using probe‐vehicle data. The results of analyses show that route choice behavior relates to the distance from driver's position to the destination and that dynamic route choice behavior is modeled better by considering decision process during the trip.     

Travelers' preferences for in-vehicle information systems: An exploratory analysis

A sample of travelers' preferences toward in-vehicle traffic information systems was undertaken and appropriate statistical models were estimated. Specifically, ordered logit and regression analyses were conducted to quantify travelers' ratings of the importance of in-vehicle system attributes, and the distance ahead that they prefer to be notified of various types of information provided by in-vehicle systems. Model estimation results show that travelers' socio-economics, habitual travel patterns, commute congestion levels and attitudes toward in-vehicle technologies are significant determinants of travelers' importance ratings and the preferred distance ahead of in-vehicle system information. These model results provide important information for both marketing and design of in-vehicle information systems.     

New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas

The percentage of the population being served by a transit system in a metropolitan region is a key system performance measure but depends heavily on the definition of service area. Observing existing service areas can help identify transit system gaps and redundancies. In the public transit industry, buffers at 400 m (0.25 miles) around bus stops and 800 m (0.5 miles) around rail stations are commonly used to identify the area from which most transit users will access the system by foot. This study uses detailed OD survey information to generate service areas that define walking catchment areas around transit services in Montreal, Canada. The 85th percentile walking distance to bus transit service is found to be around 524 m for home-based trip origins, 1,259 m for home-based commuter rail trip origins. Yet these values are found to vary based on our analysis using two statistical models. Walking distances vary based on route and trip qualities (such as type of transit service, transfers and wait time), as well as personal, household, and neighbourhood characteristics. Accordingly, service areas around transit stations should vary based on the service offered and attributes of the people and places served. The generated service areas derived from the generalized statistical model are then used to identify gaps and redundancies at the system and route level using Montreal region as an example. This study can be of benefit to transport engineers and planners trying to maximize transit service coverage in a region while avoiding oversupply of service.     

Extent and correlates of walking in the USA

This paper examines data about walking trips in the US Department of Transportation’s 2001 National Household Travel Survey. The paper describes and critiques the methods used in the survey to collect data on walking. Using these data, we summarize the extent of walking, the duration and distance of walk trips, and variations in walking behavior according to geographic and socio-demographic factors. The results show that most Americans do not walk at all, but those who do average close to thirty minutes of walking a day. Walk trips averaged about a half-mile, but the median trip distance was a quarter of a mile. A significant percentage of the time Americans’ walk was spent traveling to and from transit trips. Binary logit models are used for examining utility and recreational walk trips and show a positive relationship between walking and population density for both. For recreational trips, this effect shows up at the extreme low and high ends of density. For utility trips, the odds of reporting a walk trip increase with each density category, but the effect is most pronounced at the highest density categories. At the highest densities, a large portion of the effect of density occurs via the intermediary of car ownership. Educational attainment has a strong effect on propensity to take walk trips, for both for utility and recreation. Higher income was associated with fewer utility walk trips but more recreational trips. Asians, Latinos, and blacks were less likely to take utility walk trips than whites, after controlling for income, education, density, and car ownership. The ethnic differences in walking are even larger for recreational trips.     

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers’ observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior.     

Impact of different shopping stages on shopping-related travel behaviour: analyses of the Netherlands Mobility Panel data

From the moment e-shopping emerged, there have been speculations about its impact on personal mobility. A fair amount of research has already been carried out on Internet shopping itself as well as on its consequences for mobility. Most studies focus on the overall impact of online shopping on personal mobility. However, little is known about how personal shopping mobility can be characterised when differentiating its constituent stages, being browsing/orienting, comparing, selecting and purchasing products, and how this is affected by e-shopping. This will be the main topic of this paper. We will investigate this using recently collected data from the Netherlands Mobility Panel [in Dutch: MobiliteitsPanel Nederland (MPN)]. It is the unique combination of reported shopping trips in the three-day travel diary, the large amount of personal and household characteristics combined with the detailed information from the e-shopping questionnaire that enables us to perform this research. Using factor analysis, we explore the underlying factors related to the browsing and selection behaviour prior to the purchase of a product. Using these factors as a starting point, we apply cluster analysis resulting in three homogeneous groups of shoppers with different pre-purchase shopping behaviour. The groups differ clearly with respect to personal and household characteristics, in the frequency with which they buy and sell products online and in their perception of (dis-)advantages of online shopping. Once relevant groups have been distinguished and characterised, differences in shopping-related mobility between them are studied in two different ways. Firstly, we analyse statements from shoppers on how their shopping-related mobility has changed. Secondly, we analyse shopping trips reported in the three-day travel diary. Only one group, which consists of shoppers that rely on the Internet to search for product information, compare prices and get new product ideas, states that their shopping-related travel behaviour has changed since they started shopping online. Approximately 50% of all shoppers experienced no difference in their shopping mobility. The analysis of actual shopping mobility using the travel diary data showed only minor differences in shopping-related travel behaviour between the identified groups. Finally, we fit a multi-variate linear regression model of shopping trip distance to determine if (e)-shopping characteristics influence trip distances. The frequency with which people shop online as well as some stated changes in shopping-related travel behaviour (shopping in a similar manner and shopping longer) turn out to influence non-grocery shopping trip distance. No significant influence could be found of shopping cluster membership on shopping trip distances.

     

All work and no play? The role of instrumental and affective factors in work and leisure journeys by different travel modes

This paper examines the relative importance that people attach to various instrumental and affective journey attributes when travelling either for work or for a leisure day trip and presents how journeys by various travel modes score on these attributes. Although not a comparative paper, data are presented for two studies which used some identical measurements: one on commuter journeys and one on leisure journeys. The results show that for work journeys, respondents tend to attach more importance to instrumental aspects, and especially to convenience than to affective factors. For leisure journeys, however, respondents appear to attach almost equal importance to instrumental and affective aspects, particularly flexibility, convenience, relaxation, a sense of freedom and ‘no stress’. Each study also examines (i) how regular users’ evaluate their own mode and (ii) how car users perceive the performance of alternative modes compared to their importance ratings. This ‘gap’ analysis reveals on which modes and for which attributes the greatest deficiencies in performance lie. The data for both the work and leisure studies shows that for car users, alternative transport modes are inferior on the salient attributes such as convenience and flexibility even though car users rate modes such as walking and cycling as performing well, if not better, on less important attributes such as the environment, health and even excitement. Nevertheless, for those who cycle and walk regularly, satisfaction with their own travel mode as measured by the gap between importance and performance on salient attributes is better than for those who mostly use the car. Conclusions are made as to how greater attention to affective factors may improve our understanding of mode choice.     

Linking land use with household vehicle emissions in the central puget sound: methodological framework and findings

A leading cause of air pollution in many urban regions is mobile source emissions that are largely attributable to household vehicle travel. While household travel patterns have been previously related with land use in the literature (Crane, R., 1996. Journal of the American Planning Association 62 (1, Winter); Cervero, R. and Kockelman, C., 1997. Transportation Research Part D 2 (3), 199–219), little work has been conducted that effectively extends this relationship to vehicle emissions. This paper describes a methodology for quantifying relationships between land use, travel choices, and vehicle emissions within the Seattle, Washington region. Our analysis incorporates land use measures of density and mix which affect the proximity of trip origins to destinations; a measure of connectivity which impacts the directness and completeness of pedestrian and motorized linkages; vehicle trip generation by operating mode; vehicle miles/h of travel and speed; and estimated household vehicle emissions of nitrogen oxides, volatile organic compounds, and carbon monoxide. The data used for this project consists of the Puget Sound Transportation Panel Travel Survey, the 1990 US Census, employment density data from the Washington State Employment Security Office, and information on Seattle’s vehicle fleet mix and climatological attributes provided by the Washington State Department of Ecology. Analyses are based on a cross-sectional research design in which comparisons are made of variations in household travel demand and emissions across alternative urban form typologies. Base emission rates from MOBILE5a and separate engine start rates are used to calculate total vehicle emissions in grams accounting for fleet characteristics and other inputs reflecting adopted transportation control measures. Emissions per trip are based on the network distance of each trip, average travel speed, and a multi-stage engine operating mode (cold start, hot start, and stabilized) function.     

A topological route choice model for metro

This article presents a route choice model for public transit networks that incorporates variables related to network topology, complementing those found in traditional models based on service levels (travel time, cost, transfers, etc.) and users’ socioeconomic and demographic characteristics (income level, trip purpose, etc.). The topological variables represent concepts such as the directness of the chosen route and user knowledge of the network. For both of these factors, the necessary data is endogenous to the modelling process and can be quantified without the need for information-gathering beyond what is normally required for building route choice models. Other novel variables in the proposed formulation capture notions of user comfort such as vehicle occupancy rates and certain physical characteristics of network stations. We conclude that these new variables significantly improve the explanatory and predictive ability of existing route choice specifications.     

Analysis of factors affecting the choice of route of pedestrians

Pedestrians as compared to vehicular traffic enjoy a high degree freedom of movement even in heavily congested areas. Consequently, there are more alternative links available to pedestrians between a given origin‐destination (O‐D) pair. This paper describes a study done by the University of Calgary to evaluate the factors affecting the choice of route on intra‐CBD trips or trips within the Central Business District (CBD).

An origin destination survey conducted in downtown Calgary, Alberta enabled the identification of the most significant factors influencing the choice. These factors were analyzed in relation to the physical characteristics of the location, personal characteristics of the trip maker and the type of the trip.

It appears that most people chose the shortest link and factors such as the level of congestion, safety or visual attractions were only secondary. This suggests that the length should be made a major consideration when planning and designing pedestrian links.     

Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai

The impacts of the built environment characteristics in residential neighborhoods on commuting behavior are explored in the literature. Scant evidence, however, is provided to scrutinize the role of the built environment characteristics at job locations. Studies also overlooked the potential error correlations between commuting mode and commuting distance due to the unobserved factors that influence both variables. We examined the impacts of the built environment characteristics at both residential and job locations on commuting mode and distance, by applying a discrete-continuous copula-based model on 857 workers in Shanghai. In contrast with studies of Western countries, we showed residential built environment characteristics are more influential on commute behavior than the built environment characteristics at job locations. This suggests the importance of local specificity in policymaking process. We also found the proportion of four-way intersections, road density, and population density in residential areas are negatively associated with driving probability, with elasticity amounts of −1.00, −0.23, and −0.08, respectively. Hence, dense and pedestrian- and cyclist-oriented development help to reduce travel distance and encourage walking, biking, and transit modes of travel.     

An analysis of trip length and land use patterns in the Greater Athens Area

The research described in this paper is an attempt to quantify the impact of a certain distribution of land uses upon trip characteristics — notably trip lengths. The idea is to relate trip lengths classified by mode and purpose to the distance of one trip end from the conurbation centre. The latter is defined as the point which represents a reasonable estimate of the place where the economic, administrative, and cultural life of the urban area is centered.By relating trip lengths to the distance of one trip end from the centre, one could obtain a relation which in effect would be a quantitative expression of the relation between transport and land use. The first application of this idea was in London using the 1966 journey to work data, and it gave quite satisfactory results.The area examined in this research is the Greater Athens Area. The method of analysis is similar to that followed in London so the results of the two studies can be compared. Only work trips are considered for four modes: car, bus, train and all modes (total). It is found that in the case of Athens too, when distance of the workplace from the centre is considered, trip lengths change in smoothly varying ways and a series of mathematical curves can be fitted to the data with an acceptable degree of accuracy. These curves are of the Gamma family having a constant spread factor and varying scale factors for each mode considered. When the distance of the residence end of the trip from the centre is considered, the trip length distributions are not very smooth, a clear mathematical curve cannot be fitted, but again a considerable degree of order can be detected. In addition to the above results a discussion is given on their meaning and the possibilities for future research. In fact the results so far are considered to be the first stage of a more extended research programme which will eventually connect trip length distributions to income and other economic or social parameters in an urban area.The author wishes to express his thanks and appreciation for the comments and constructive criticism made on the various drafts of this paper by M.J.M.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.