Impacts of improvements in rural roads on household income through the enhancement of market accessibility in rural areas of Cambodia

Rural areas in low-income countries often face severe poverty typically caused by insufficient accessibility to basic facilities. Improvements in rural roads are expected to reduce poverty although the mechanism has not been investigated sufficiently. This study empirically analyzes the impacts of rural road improvements implemented from 2012 to 2014 in Cambodia, highlighting local residents’ accessibility to local markets. This study assumes two causal relationships: rural road improvements have upgraded the accessibility and travel frequency to local markets, and the upgraded accessibility and travel frequency to local markets have led to a growth in local residents’ income. The hypotheses are statistically tested with a dataset developed through a questionnaire survey conducted in three areas in 2016. The dataset contains responses from 400 local residents to questions concerning their social attributes, livelihoods, travel modes, travel frequency, and time/cost of travels to the basic facilities. The quasi-experimental design incorporating a difference-in-differences design and an inverse possibility of treatment weighting approach revealed that the improvements in rural roads did not affect travel time nor travel cost but significantly enhanced travel frequency to local markets, and that an increase in the travel frequency to local markets and travel time savings significantly contributed to the households’ income growth. The results suggest that the improvement of seasonal reliability in accessing local markets through an introduction of all-weather roads could be critical to enhance household income, particularly in areas where agriculture is a leading industry and weather conditions are unstable across seasons.

     

A multiobjective hierarchical model for locating public facilities on a transportation network: A goal programming approach

This paper introduces a Multiobjective Hierarchical Model (MOHLM) for locating public facilities on a transportation network. The proposed model combines the multiobjective nature of the location-allocation problem with the hierarchical character of some public service systems, such as health care delivery. The model examines both maximum and total weighted travel time, facility utilization, and total travel time from the master facility to the attached subordinate facilities. An iterative goal programing algorithm is used to solve the problem. An example related to the location of health care facilities in a rural area of Greece is used to illustrate the application of the proposed model.     

Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes

Emerging autonomous vehicles (AVs) and shared mobility systems per se will transform urban passenger transportation. Coupled together, shared AVs (SAVs) can facilitate widespread use of shared mobility services by providing flexible public travel modes comparable to private AV. Hence, it may be conjectured that future urban mobility is likely an on-demand service and AV private ownership is unappealing. Nonetheless, it is still unclear what observable and latent factors will drive public interest in (S)AVs, the answer to which will have important implications on transportation system performance. This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Development of location-based services for recommending departure stations to park and ride users

More and more commuters are beginning to favour public transportation. Fast and convenient park and ride (PnR) services provided by public transportation authorities are the result of changes of household demographics and household, increasing fuel prices and a focus on environmental sustainability. However, lack of parking spaces in PnR facilities creates a major bottleneck to this service. The aim of this research is to develop a location-based service (LBS) application to help PnR users choose the best train station to use to reach their destination using a multicriteria decision making model. A fuzzy logic method is used to estimate parking availability when a user is estimated to arrive at a PnR facility. Two surveys are conducted to collect traffic flow, travel behaviour and service quality data at four selected Perth Western Australia train stations. With the proposed approach and survey data, a prototype of LBS application, Station Finder, was developed using the Android SDK 4.0 and Google API 16. This application is a useful and practical tool to save travel cost and time of PnR users’.     

Transportation serviceability analysis for metropolitan commuting corridors based on modal choice modeling

Major commuting corridors in metropolitan areas generally comprise multiple transportation modes for commuters, such as transit (subways or buses), private vehicles, or park-and-ride combinations. During the morning peak hour, the commuters would choose one of the available transportation modes to travel through the corridors from rural/suburban living areas to urban working areas. This paper introduces a concept of transportation serviceability to evaluate a transportation mode’s service status in a specific link, route, road, or network during a certain period. The serviceability can be measured by the possibility that travelers choose a specific type of transportation service at a certain travel cost. The commuters’ modal-choice possibilities are calculated using a stochastic equilibrium model based on general travel cost. The modeling results illustrate how transportation serviceability is influenced by background traffic flow in a corridor, value of comfort for railway mode, and parking fee distribution.     

Dynamic Modal Accessibility Gap: Measurement and Application Using Travel Routes Data

Accessibility is a key concept in transportation research and an important indicator of people’s quality of life. With the development of big data analytics, dynamic accessibility that captures the temporal variations of accessibility becomes an important research focus. Few prior studies focus on comparative measures of dynamic accessibility to Points of Interest (POIs) by alternative travel modes. To fill this research gap, we propose a new index called dynamic modal accessibility gap (DMAG), which draws upon available data on residents’ real travel routes using different travel modes, as well as the data on POIs. We study the DMAG in the real-travel covered area, assuming POIs are only useful if it is within someone’s real-travel covered area. We then apply this DMAG methodology to Shanghai’s central city and peripheral area. In both cases, we measure the accessibility for public and private travel modes. As an example, one-week taxi GPS and metro smart card data, and POIs data are used to generate the DMAG index for 30-minute and 60-minute trip durations for weekdays and holidays. Results show that DMAG can reflect the pattern of temporal variations. The proposed DMAG analytical framework, which can be applied at both the user and the system levels, can support urban and transportation planning, and promote social equity and livability.     

Use characteristics and demographics of rural transit riders: a case study in Tennessee

Intercity bus (ICB), deviated fixed route transit (DFRT) and demand responsive transit (DRT) are three major modes of rural public transportation. This paper focuses on the characteristics and motivations of DFRT and DRT riders, compared to non-riders, in Tennessee. A rural DFRT rider survey, a rural DRT rider survey and a rural (non-rider) resident survey were performed. It is found that DFRT and DRT riders have similar demographics to ICB riders. The most common trip purpose for DFRT and DRT passengers is medical care, which is different from ICB trips. Ninety percent of the riders have difficulty finding alternative transportation modes, suggesting they are captive riders, not choice riders. Regression results indicate that people choosing transit modes tend to have lower personal and household income, own fewer cars, to not be homeowners, and be of non-white race. Rural residents who receive more education are more likely to be open-minded to use rural transit.     

An agent-based multimodal simulation model for capacity planning of a cross-border transit facility

Cross-border transit facilities constitute major public investment, and thus must serve the long-term needs of the communities, such as providing access to schools and businesses, contributing to a shared regional culture and lifestyle, fostering international trade, and supporting jobs for the region’s residents. Numerous studies have been conducted to evaluate the economic implications of vehicular flow delays at border crossings, however none of the studies focused on assessing cross-border flow of bus passengers and pedestrians. Since pedestrians are considered to be autonomous, intelligent, and perceptive, it is a challenging task to predict pedestrian movement and behavior in comparison to vehicular flows which follow a specific set of traffic rules. This paper presents a multiagent based multimodal simulation model to evaluate the capacity and performance of a cross-border transit facility. The significance of this research is the use of dynamic mode choice functionality in the model, which allows an individual person to make instantaneous choices between available modes of transportation. The scope of interest of the paper is limited to simulating access interface, circulation areas, ancillary and processing facilities. The developed model was calibrated to ensure realistic performance, and validated against specific performance criteria such as throughput per processing facility. In order to demonstrate the applicability of the developed simulation model, capacity and operational planning of a pedestrian transit facility was performed. The relative performance of alternative design or configuration was evaluated using the level of service criteria. Lastly, the effectiveness of each proposed capacity or operational improvement strategy was compared to the “do-nothing” scenario.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

Inviting travelers to the smorgasbord of sustainable urban transport: evidence from a MaaS field trial

A Mobility-as-a-Service (MaaS) concept, UbiGo, was implemented in Gothenburg, Sweden, and used for a 6-month period by 195 individuals in 83 households. Four participant subgroups were identified: Car shedders, Car accessors, Simplifiers, and Economizers. A qualitative analysis revealed that the subgroups had different reasons to join the service and different expectations of the change that would occur on the basis of the altered preconditions offered by the service. Previous car users reduced their use of private car and increased their use of public transport and active modes. Participants who did not have access to a privately-owned car but thought they needed one discovered that they managed well without. Other participants were reinforced in their existing behaviors but in ways they did not envisage, depending on which goals they had at the outset of the trial. Overall, the participants were also satisfied with the service, as well as with stated changes and non-changes, even if this in some cases meant more planning. Based on the empirical findings it could be argued that a service approach, such as UbiGo, has the potential to reduce the need for private car ownership, and enable people to change their mode choices and travel patterns. The potential relies however on a number of specific features of the service of which flexibility and a need- rather than a mode-based approach are key features.     

Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions

This paper examines the life-cycle inventory impacts on energy use and greenhouse gas (GHG) emissions as a result of candidate travelers adopting carsharing in US settings. Here, households residing in relatively dense urban neighborhoods with good access to transit and traveling relatively few miles in private vehicles (roughly 10% of the U.S. population) are considered candidates for carsharing. This analysis recognizes cradle-to-grave impacts of carsharing on vehicle ownership levels, travel distances, fleet fuel economy (partly due to faster turnover), parking demand (and associated infrastructure), and alternative modes. Results suggest that current carsharing members reduce their average individual transportation energy use and GHG emissions by approximately 51% upon joining a carsharing organization. Collectively, these individual-level effects translate to roughly 5% savings in all household transport-related energy use and GHG emissions in the U.S. These energy and emissions savings can be primarily attributed to mode shifts and avoided travel, followed by savings in parking infrastructure demands and fuel consumption. When indirect rebound effects are accounted for (assuming travel-cost savings is then spent on other goods and services), net savings are expected to be 3% across all U.S. households.     

The relationship between the built environment and nonwork travel: A case study of Northern California

Many studies have found that residents living in suburban neighborhoods drive more and walk less than their counterparts in traditional neighborhoods. This evidence supports the advocacy of smart growth strategies to alter individuals’ travel behavior. However, the observed differences in travel behavior may be more of a residential choice than a travel choice. Applying the seemingly unrelated regression approach to a sample from Northern California, we explored the relationship between the residential environment and nonwork travel frequencies by auto, transit, and walk/bicycle modes, controlling for residential self-selection. We found that residential preferences and travel attitudes (self-selection) significantly influenced tripmaking by all three modes, and also that neighborhood characteristics (the built environment and its perception) retained a separate influence on behavior after controlling for self-selection. Both preferences/attitudes and the built environment itself played a more prominent role in explaining the variation in non-motorized travel than for auto and transit travel. Taken together, our results suggest that if cities use land use policies to offer options to drive less and use transit and non-motorized modes more, many residents will tend to do so.     

Do modes of transportation and GPS affect cognitive maps of Londoners?

Legibility has long been recognized as an important factor in creating a good image of a city in individuals’ minds. This image is perceived to assist people in understanding the city, finding their way, and recalling the city. The quality of the image affects individuals’ abilities in way-finding. This is especially important for cosmopolitan and global cities such as London in order to preserve resources and time, manage travel costs, limit pollution (air or noise) and enhance these cities as places to live, work and visit. This research examines the cognitive maps of London drawn by a sample of its residents to discover how different modes of transportation and GPS usage could affect individuals’ urban images. Such research is useful for town planners, local government departments, and urban and transport planners because of the way it considers the legibility of London as and provides a tool to study individuals’ urban images. 101 participants were recruited with at least a two-year residency from both genders (38.6% females and 61.4% males) with the average age of 33.88 and S.D. = 10.63. The results suggest car use has a positive correlation with seeing London in city scale and GPS usage has a negative correlation. Whilst recent studies have shown that there are differences between active travel modes (e.g., walking, bicycle riding or driving a car) and passive modes (e.g., as a passenger taking a bus, train or taxi), this study indicates that GPS usage also influences cognitive maps, with a negative correlation found between GPS usage and drawing maps on a city scale. Other significant associations were found for the car drivers with a positive relation with the number of roads mentioned on the maps, seeing London in city scale and having a two-dimensional façade image of the city in mind.     

Prioritizing bicycle paths in Belo Horizonte City,Brazil: Analysis based on user preferences and willingness considering individual heterogeneity

Using bicycles as a commuting mode has proven to be beneficial to both urban traffic conditions and travelers’ health. In order to efficiently design facilities and policies that will stimulate bicycle use, it is necessary to first understand people’s attitudes towards bicycle use, and the factors that may influence their preferences. Such an understanding will enable reliable predictions of bicycle use willingness level, based on which cycling facility construction can be reasonably prioritized.As people often have different perceptions on exercising, green transportation, and traffic conditions, effects of potentially influencing factors on people’s willingness of using bicycles tend to be highly heterogeneous. This paper uses a random parameter ordered probit model to analyze how travelers’ willingness of using bicycles is influenced by various socio-economic factors in Belo Horizonte City, Brazil, with the consideration of individual heterogeneity. The data was collected through the 2010 bicycle use survey in Belo Horizonte City. Results show that, first, the willingness of using bicycle is favored by middle income class household, and negatively related with commuting time. Second, people who rent apartments tend to be more willing to use bicycles. Third, if a person is currently walking a long time to work, he/she would be most willing to commute with a bicycle in the future. Those currently commuting a relatively short distance by motorcycle and bus follow this group in terms of willingness to commute by bicycle in the future. Car users seem to be difficult to convert to bicycle users. Moreover, the estimation shows clear evidence that significant individual heterogeneity indeed exists, especially for education level, necessitating the consideration of such an effect. With the calibrated model, residents’ willingness of using bicycle commuting is then estimated for the entire Belo Horizonte City using the 2010 Census and the 2012 O/D survey data. The results are cross validated using the bicycle path preference information, also obtained from the 2010 bicycle use survey.     

Using the revenues from congestion pricing

The economic theory behind congestion pricing relies on using the revenues to help compensate highway users. But can practical methods of using revenues come close to achieving this compensation, and still have salient appeal to important political groups? This paper investigates the possibilities for designing a package of revenue uses that can achieve these twin goals. The suggested approach returns two-thirds of the revenues to travelers through travel allowances and tax reductions, and uses the rest to improve transportation throughout the area, including affected business centers. By replacing regressive sales and fuel taxes, this approach offsets the tendency of the prices alone to have a regressive distributional impact. By lowering taxes, funding new highways, improving transit, and upgrading business centers, the package provides inducements for support from several key interest groups. The potential amounts of money involved are discussed using nationwide data, and in more detail using a case study of ubiquitous facility pricing throughout the Los Angeles region. Illustrative calculations of the effects on various individuals confirm that such a package can create net benefits for a wide spectrum of people and interest groups.     

Willingness to pay for public transportation options for improving the quality of life of the rural elderly

Transportation for the rural elderly is an increasing concern as baby boomers age and young people continue to exit rural communities. When the elderly are no longer able to drive, they rely on alternative forms of transportation, including public transportation systems. Currently, such systems are usually not good substitutes for driving a private car, especially in rural areas. Because expanded rural transportation systems would likely be funded by taxpayers, an understanding of their preferences and willingness to pay (WTP) for non-medical transportation options is essential. To help understand WTP and preferences, a choice experiment survey was administered to taxpayers in three counties (Atascosa, Polk, and Parker) in Texas. Results indicate taxpayers’ value transportation services for the elderly and are willing to support them. They value more flexible options over base levels of the attributes presented, but they may not always prefer the most flexible options. Respondents’ WTP for the same transportation attribute was similar across counties, but differences in socio-demographic coefficients suggest that transportation systems may need to be customized to meet local needs. Furthermore, county residents’ WTP may not cover the cost of desired improvements to the transportation systems.     

Effect of land use on decisions of shopping tour generation: A case study of three traditional neighborhoods in WA

This study investigates the relationship between land use and shopping tour generation using an activity-based shopping model that captures the effects of land use patterns on household decisions of shopping tour frequency, tour scheduling and mode choice. The model was calibrated using travel data collected in three traditional neighborhoods located in the Puget Sound region, WA, and shopping travel patterns across seven common household structures were analyzed. The results reveal that land use patterns have virtually no impact on overall shopping tour frequency. However, land use does seem to be associated with decisions about the type of shopping tours undertaken. For example, households with poorer accessibility tend to make fewer one-stop shopping tours, and are more likely to combine shoppingtrips with other trips to form multi-stop shopping tours as a means of compensating for locational deficiencies. Finally, we also found that traditional neighborhood residents who live closer to the neighborhood commercial street, and thus, have greater accessibility, are more inclined to use non-auto modes for one-stop shopping tours.     

When is getting there half the fun? Modeling the liking for travel

This paper analyzes empirically measured values of Travel Liking––how much individuals like to travel, in various overall, mode-, and purpose-based categories. The study addresses two questions: what types of people enjoy travel, and under what circumstances is travel enjoyed? We first review and augment some previously hypothesized reasons why individuals may enjoy travel. Then, using data from 1358 commuting residents of three San Francisco Bay Area neighborhoods, a total of 13 ordinary least-squares linear regression models are presented: eight models of short-distance Travel Liking and five models of long-distance Travel Liking. The results indicate that travelers’ attitudes and personality (representing motivations) are more important determinants of Travel Liking than objective travel amounts. For example, while those who commute long distances do tend to dislike commute travel (as expected), the variables entering the models that hold the most importance relate to the personality and attitudes of the traveler. Most of the hypothesized reasons for liking travel are empirically supported here.