Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility

In the past decade, many studies have explored the relationship between travelers’ travel mode and their trip satisfaction. Various characteristics of the chosen travel modes have been found to influence trip experiences; however, apart from the chosen modes, travelers’ variability in mode use and their ability to vary have not been investigated in the trip satisfaction literature. This current paper presents an analysis of commuting trip satisfaction in Beijing with a particular focus on the influence of commuters’ multimodal behavior on multiple workdays and their modal flexibility for each commuting trip. Consistent with previous studies, we find that commuting trips by active modes are the most satisfying, followed by trips by car and public transport. In Beijing, public transport dominates. Urban residents increasingly acquire automobiles, but a strict vehicle policy has been implemented to restrict the use of private cars on workdays. In this comparatively constrained context for transport mode choice, we find a significant portion of commuters showing multimodal behavior. We also find that multimodal commuters tend to feel less satisfied with trips by alternative modes compared with monomodal commuters, which is probably related to their undesirable deviation from habitual transport modes. Furthermore, the relationship between modal flexibility and trip satisfaction is not linear, but U-shaped. Commuters with high flexibility are generally most satisfied because there is a higher possibility for them to choose their mode of transport out of preference. Very inflexible commuters can also reach a relatively high satisfaction level, however, which is probably caused by their lower expectations beforehand and the fact that they did not have an alternative to regret in trip satisfaction assessments.     

Determinants of ground transport modal choice in long-distance trips in Spain

In transport economics, modeling modal choice is a fundamental key for policy makers trying to improve the sustainability of transportation systems. However, existing empirical literature has focused on short-distance travel within urban systems. This paper contributes to the limited number of investigations on mode choice in medium- and long-distance travel. The main objective of this research is to study the impacts of socio-demographic and economic variables, land-use features and trip attributes on long-distance travel mode choice. Using data from 2007 Spanish National Mobility Survey we apply a multilevel multinomial logit model that accounts for the potential problem of spatial heterogeneity in order to explain long-distance travel mode choice. This approach permits us to compute how the probability of choosing among private car, bus and train varies depending on the traveler spatial location at regional level. Results indicate that travelers characteristics, trip features, cost of usage of transport modes and geographical variables have significant impacts on long-distance mode choice.     

Comparing travel mode and trip chain choices between holidays and weekdays

Choices of travel mode and trip chain as well as their interplays have long drawn the interests of researchers. However, few studies have examined the differences in the travel behaviors between holidays and weekdays. This paper compares the choice of travel mode and trip chain between holidays and weekdays tours using travel survey data from Beijing, China. Nested Logit (NL) models with alternative nesting structures are estimated to analyze the decision process of travelers. Results show that there are at least three differences between commuting-based tours on weekdays and non-commuting tours on holidays. First, the decision structures in weekday and holiday tours are opposite. In weekday tours people prefer to decide on trip chain pattern prior to choosing travel mode, whereas in holiday tours travel mode is chosen first. Second, holiday tours show stronger dependency on cars than weekday tours. Third, travelers on holidays are more sensitive to changes in tour time than to the changes in tour cost, while commuters on weekdays are more sensitive to tour cost. Findings are helpful for improving travel activity modeling and designing differential transportation system management strategies for weekdays and holidays.     

Modeling traveler mode choice behavior of a new high-speed rail corridor in China

This study examines mode choice behavior for intercity business and personal/recreational trips. It uses multinomial logit and nested logit methods to analyze revealed preference data provided by travelers along the Yong-Tai-Wen multimodal corridor in Zhejiang, China. Income levels are found to be positively correlated with mode share increases for high-speed rail (HSR), expressway-based bus, and auto modes, while travel time and trip costs are negatively correlated with modal shift. Longer distance trips trigger modal shifts to HSR services but prevent modal shift to expressway-based auto use due to escalation of fuel cost and toll charges. Travelers are less elastic in their travel time and cost for trips by nonexpressway-based auto use modes. The magnitude of elasticity for travel time is higher than trip costs for business trips and lower for personal/recreational trips. The study provides some policy suggestions for transportation planners and decision-makers.     

Exploring key determinants of travel satisfaction for multi-modal trips by different traveler groups

Using a primary dataset from an experimental survey in eight European cities, this study identified the key determinants of satisfaction with individual trip stages as well as overall journey experience for different travel modes and traveler groups. Multivariate statistical analyses were used to examine the relationships between overall satisfaction and travel experience variables, trip complexity, subjective well-being indices, travel-related attitudes as well as individual- and trip-specific attributes. The results indicate that for certain traveler groups, such as women, young and low-income or unemployed travelers, there are distinctive determinants of satisfaction with trip stages for various travel modes. The results also indicate that satisfaction with the primary trip stage is strongly linked to overall trip satisfaction, while satisfaction levels with access and egress trip stages are strongly related to satisfaction with the primary trip stage. Past experience, traveler expectations and attitudes, and the emotional state of travelers are also significant explanatory variables for travel satisfaction. The results indicate that when an individual consciously chooses a particular travel mode, they will report a higher level of satisfaction with that chosen mode. Notwithstanding, while past experience highly influences an individual’s current travel satisfaction, the more they travel with the current mode, the less satisfied they are with their choice. The results of this study highlight the importance of gaining a better understanding of the interaction between instrumental variables and non-instrumental variables at different trip stages and the influence on user preferences, satisfaction and decision-making processes.     

Tradable credits scheme and transit investment optimization for a two‐mode traffic network

This paper proposes an optimization model to minimize the “system costs” and guide travelers' behavior by exploring the optimal bus investment and tradable credits scheme design in a bimodal transportation system. Travelers' transport mode choice behavior (car or bus) and the modal equilibrium conditions between these two forms of transport are studied in the tradable credits scheme. Public transport priority is highlighted by charging car travelers credits only. The economies of scale presented by the transit system under the tradable credit scheme are analyzed by comparing the marginal cost and average cost. Numerical examples are presented to demonstrate the model. Furthermore, the effects of tradable credits schemes on bus investment and travelers' modal choice behavior are explored based on scenario discussions. Copyright © 2016 John Wiley & Sons, Ltd.     

Analyzing commuter train user behavior: a decision framework for access mode and station choice

The purpose of the current research effort is to develop a framework for a better understanding of commuter train users’ access mode and station choice behavior. Typically, access mode and station choice for commuter train users is modeled as a hierarchical choice with access mode being considered as the first choice in the sequence. The current study proposes a latent segmentation based approach to relax the hierarchy. In particular, this innovative approach simultaneously considers two segments of station and access mode choice behavior: Segment 1—station first and access mode second and Segment 2—access mode first and station second. The allocation to the two segments is achieved through a latent segmentation approach that determines the probability of assigning the individual to either of these segments as a function of socio-demographic variables, level of service (LOS) parameters, trip characteristics, land-use and built environment factors, and station characteristics. The proposed latent segment model is estimated using data from an on-board survey conducted by the Agence Métropolitaine de Transport for commuter train users in Montreal region. The model is employed to investigate the role of socio-demographic variables, LOS parameters, trip characteristics, land-use and built environment factors, and station characteristics on commuter train user behavior. The results indicate that as the distance from the station by active forms of transportation increases, individuals are more likely to select a station first. Young persons, females, car owners, and individuals leaving before 7:30 a.m. have an increased propensity to drive to the commuter train station. The station model indicates that travel time has a significant negative impact on station choice, whereas, presence of parking and increased train frequency encourages use of the stations.     

Factors influencing the choice of shared bicycles and shared electric bikes in Beijing

China leads the world in both public bikeshare and private electric bike (e-bike) growth. Current trajectories indicate the viability of deploying large-scale shared e-bike (e-bikeshare) systems in China. We employ a stated preference survey and multinomial logit to model the factors influencing the choice to switch from an existing transportation mode to bikeshare or e-bikeshare in Beijing. Demand is influenced by distinct sets of factors: the bikeshare choice is most sensitive to measures of effort and comfort while the e-bikeshare choice is more sensitive to user heterogeneities. Bikeshare demand is strongly negatively impacted by trip distance, temperature, precipitation, and poor air quality. User demographics however do not factor strongly on the bikeshare choice, indicating the mode will draw users from across the social spectrum. The e-bikeshare choice is much more tolerant of trip distance, high temperatures and poor air quality, though precipitation is also a highly negative factor. User demographics do play a significant role in e-bikeshare demand. Analysis of impact to the existing transportation system finds that both bikeshare and e-bikeshare will tend to draw users away from the “unsheltered modes”, walk, bike, and e-bike. Although it is unclear if shared bikes are an attractive “first-and-last-mile solution”, it is clear that e-bikeshare is attractive as a bus replacement.     

Patterns of car usage and restraint modelling

Transport demand forecasting procedures have traditionally employed household based modal split models implicitly assuming a selection of mode for each trip based on relative generalised cost. A detailed examination of the trip patterns of a sample of household in West Yorkshire shows that in fact there is little discretionary choice of public transport; public transport trips in car owning households generally being explained in terms of the specific unavailability of the car for such trips. Two versions of a category analysis model for modal split are based on this observation and applied to household data for Glamorgan and Monmouthshire to show that such a procedure is workable and produces results comparing favourably with traditional approaches. The likely implications of three types of restraint policy are examined and it is concluded that the existing interdependence in trip patterns and modal choice within the household is of great significance in determining their effects. In particular it appears that positive attempts to increase vehicle occupancy at the peak are likely to be more favourable to public transport finances than the more negative policies to restrain use of the car for journey to work, or second car ownership.     

Optimal transit fare in a bimodal network under demand uncertainty and bounded rationality

This paper investigates the optimal transit fare in a simple bimodal transportation system that comprises public transport and private car. We consider two new factors: demand uncertainty and bounded rationality. With demand uncertainty, travelers are assumed to consider both the mean travel cost and travel cost variability in their mode choice decision. Under bounded rationality, travelers do not necessarily choose the travel mode of which perceived travel cost is absolutely lower than the one of the other mode. To determine the optimal transit fare, a bi‐level programming is proposed. The upper‐level objective function is to minimize the mean of total travel cost, whereas the lower‐level programming adopts the logit‐based model to describe users' mode choice behaviors. Then a heuristic algorithm based on a sensitivity analysis approach is designed to solve the bi‐level programming. Numerical examples are presented to illustrate the effect of demand uncertainty and bounded rationality on the modal share, optimal transit fare and system performance. Copyright © 2013 John Wiley & Sons, Ltd.     

Attitudinal and situational variables influencing urban mode choice: Some empirical findings

A mode choice decision structure incorporating traveler attitudes toward modes and situational constraints is investigated. The major hypothesis tested is that mode choice is determined primarily by situational constraints, such as auto ownership and income, secondly by the quality of alternative modes.The structure of the mode choice process is analyzed with respect to (1) applicability of certain choice criterion forms; (2) psychological weighting of modal attributes in the choice criterion; (3) strength of logit, probit, and discriminant functional forms; (4) the relative strength of socio-economic and attitudinal variables in predicting mode choice. An evaluation is made of 50 binary choice models fitted to a sample of 471 randomly drawn urban travelers. Results indicate that (1) the four choice criterion forms tested are all about equal in predictive strength; (2) psychological weighting has no effect on model strength, but does influence which modal attributes appear to determine choice; (3) the three functional forms tested are all about equal in strength; (4) situational factors account for 80–90% of variation explained by the models, attitudes toward modes 10–20%, thus confirming the primary hypothesis. Implications of these results for mode choice modeling and transit planning are discussed.This paper summarizes current research at the New York State Department of Transportation on the motivations and causes of travel behavior. Complete findings are available in Hartgen (1973).     

The relationship between land use and intrazonal trip making behaviors: Evidence and implications

This paper addresses the relationship between land use, destination selection, and travel mode choice. Specifically, it focuses on intrazonal trips, a sub-category of trip making where both trip origin and trip destination are contained in the same geographic unit of analysis, using data from the 1994 Household Activity and Travel Diary Survey conducted by Portland Metro. Using multinomial logit and binary logistic models to measure travel mode choice and decision to internalize trips, the evidence supports the conclusions that (1) intrazonal trips characteristics suggest mode choice for these trips might be influenced by urban form, which in turn affects regional trip distribution; (2) there is a threshold effect in the ability of economic diversity/mixed use to alter travel behavior; and (3) greater emphasis to destinations within the area where an individual’s home is located needs to be given in trip distribution models.     

Rescuing the captive [mode] user: an alternative approach to transport market segmentation

The prevailing approach to transport market segmentation which identifies two distinct groups, “captive” and “choice” users, has widely been used by professionals and scholars despite the ambiguity associated with these terms. Furthermore, conflicting interpretations from the point of view of decision makers and individuals may result in negative policy implications where the needs of captive users are neglected in favour of attracting new users. This study attempts to address these concerns by proposing an alternative segmentation framework that could be applied to any mode of transport, in any regional context, by users and decision makers alike to better guide the development of transport policies. Using the results of a large-scale transportation survey, a series of clustering techniques are employed to derive this alternative approach for segmenting walkers, cyclists, transit and automobile users. The main factors considered in the final clustering analysis are the level of trip satisfaction and practicality. The analysis yielded four market segments: captivity, utilitarianism, dedication and convenience. Using this theoretical framework to understand the distribution of travellers among market segments is essential in identifying distinct and appropriate policy interventions to improve trip conditions. It is hoped that the segmentation approach and policy framework proposed here will encourage a better balance between pragmatic and idealistic goals in transportation policy.     

Intercept and recall: Examining avidity carryover in on-site collected travel data

This study estimates trip demand and economic benefits for visitors to recreation sites when past season trip information is elicited from travelers intercepted on-site. We use a weighting function for past season counts that is different from, but nests, the standard on-site correction appropriate for current season counts. We find that for our sample of lake visitors relatively stronger preference or “avidity” for the interview site carries over across seasons. We further show that the appropriate weighting of past trip counts is critical in deriving meaningful estimates of travel demand and economic benefits.     

Avoiding specification errors in estimating the value of time

Sources of specification as well as measurement error in previous estimates of the value of time are examined in this paper, and a new set of estimates avoiding these errors is presented. The sources of specification error being analyzed are: (1) failure to take account of drivers' uncertainty in making their choices between alternative routes or modes, which was found empirically to bias the value of time estimate 50 percent or more; (2) the differential in comfort and similar intangible characteristics between two modes; and (3) the inability to account for cross-time substitutions by travelers in peak-hour conditions. Ways of avoiding these sources of error are suggested and implemented. The choice between “reported” and “measured” data is re-examined, with an emphasis on random rather than systematic perception errors by travelers. Some resulting estimates are: $1.91 per hour per adult for off-peak work trips, $2.08 for off-peak social-recreational trips, and $5.17 for peak-hour work trips.     

Forecasting high-speed rail ridership using a simultaneous modeling approach

Abstract

The newly launched, June 2009, US High-Speed Intercity Passenger Rail Program has rekindled a renewed interest in forecasting high-speed rail (HSR) ridership. The first step to the concerted effort by the federal, state, rail, and other related agencies to develop a nationwide HSR network is the development of credible approaches to forecast the ridership. This article presents a nested logit/simultaneous choice model to improve the demand forecast in the context of intercity travel. In addition to incorporating the interrelationship between trip generation and mode choice decisions, the simultaneous model also provides a platform for the same utility function flowing between both the decision-making processes. Using American Travel Survey data, supplemented by various mode parameters, the proposed model improves the forecast accuracy and confirms the significant impact of travel costs on both mode choice and trip generation. Furthermore, the cross elasticity of mode choice and trip generation related to travel costs and other modal characteristics may shed some light on transportation policies in the area of intercity travel, especially in anticipation of HSR development.     

Capturing tour mode and activity choice interdependencies: A co-evolutionary logit modelling approach

It is generally assumed that the choice of transport mode and the choice of including intermediate activities on a work tour are interrelated, but little is known about the nature of the causal relationship. To shed light on this, this paper addresses the question of whether transport mode choice is dependent on the activity choice or vice-versa. A new methodology, referred to as the co-evolutionary approach, is combined with a set of MNL models, one for each choice facet involved, to derive an indication of the order of decisions on an individual level. The models are estimated based on the work tours of a large sample of individuals in the Netherlands. The results suggest that there is substantial variation in the order of the transport mode and activity decisions. However, in the majority of cases the activity decision is made before the mode decision, suggesting that the transport mode and, in particular, the choice between car and public transport is most often ‘adjusted’ to the choice of trip chaining rather than the other way round.     

Tracking a system of shared autonomous vehicles across the Austin,Texas network using agent-based simulation

This study provides a large-scale micro-simulation of transportation patterns in a metropolitan area when relying on a system of shared autonomous vehicles (SAVs). The six-county region of Austin, Texas is used for its land development patterns, demographics, networks, and trip tables. The agent-based MATSim toolkit allows modelers to track individual travelers and individual vehicles, with great temporal and spatial detail. MATSim’s algorithms help improve individual travel plans (by changing tour and trip start times, destinations, modes, and routes). Here, the SAV mode requests were simulated through a stochastic process for four possible fare levels: $0.50, $0.75, $1, and $1.25 per trip-mile. These fares resulted in mode splits of 50.9, 12.9, 10.5, and 9.2% of the region’s person-trips, respectively. Mode choice results show longer-distance travelers preferring SAVs to private, human-driven vehicles (HVs)—thanks to the reduced burden of SAV travel (since one does not have to drive the vehicle). For travelers whose households do not own an HV, SAVs (rather than transit, walking and biking) appear preferable for trips under 10 miles, which is the majority of those travelers’ trip-making. It may be difficult for traditional transit services and operators to survive once SAVs become available in regions like Austin, where dedicated rail lines and bus lanes are few. Simulation of SAV fleet operations suggest that higher fare rates allow for greater vehicle replacement (ranging from 5.6 to 7.7 HVs per SAV, assuming that the average SAV serves 17–20 person-trips per day); when fares rise, travel demands shift away from longer trip distances. Empty vehicle miles traveled by the fleet of SAVs ranged from 7.8 to 14.2%, across the scenarios in this study. Implications of mobility and sustainability benefits of SAVs are also discussed in the paper.     

Barriers to Passenger Rail Use: A Review of the Evidence

Research has shown that even when rail travel is the most cost-effective mode of transport for a particular journey, many travellers will still choose other modes. This indicates the existence of non-financial barriers to rail use, and this paper reviews the evidence on the importance of such barriers, focusing particularly on the UK but also considering research from other countries. A total of 37 distinct barriers were identified, and these can be divided into “hard”, “soft” and “complementary” factors. Travellers are unlikely to consider these barriers individually, viewing them instead as a package, which can make it difficult to identify which barriers are most significant. In many cases, all barriers which exist for a particular traveller will need to be addressed before mode shift occurs. After considering the relative importance of the different barriers, the paper concludes by making some suggestions as to the most effective ways in which these barriers can be overcome and mode shift to rail achieved. This has key implications for transport policy, as it can inform the targeting of the limited funds available to influence travel behaviour and increase the sustainability of travel patterns.     

Transit competitiveness in polycentric metropolitan regions

This paper analyzes the potential to, and impacts of, increasing transit modal split in a polycentric metropolitan area – the Philadelphia, Pennsylvania region. Potential transit riders are preselected as those travelers whose trips begin and end in areas with transit-supportive land uses, defined as “activity centers,” areas of high-density employment and trip attraction. A multimodal traffic assignment model is developed and solved to quantify the generalized cost of travel by transit services and private automobile under (user) equilibrium conditions. The model predicts transit modal split by identifying the origin–destination pairs for which transit offers lower generalized cost. For those origin–destination pairs for which transit does not offer the lowest generalized cost, I compute a transit competitiveness measure, the ratio of transit generalized cost to auto generalized cost. The model is first formulated and solved for existing transit service and regional pricing schemes. Next, various transit incentives (travel time or fare reductions, increased service) and auto disincentives (higher out of pocket expenses) are proposed and their impacts on individual travel choices and system performance are quantified. The results suggest that a coordinated policy of improved transit service and some auto disincentives is necessary to achieve greater modal split and improved system efficiency in the region. Further, the research finds that two levels of coordinated transit service, between and within activity centers, are necessary to realize the greatest improvements in system performance.