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.

     

Temporal transferability of updated alternative-specific constants in disaggregate mode choice models

Methods of updating disaggregate discrete choice models have been proposed as a means of obtaining better transferability. However, the temporal transferability of models updated for better spatial transferability has rarely been analysed, and the factors affecting temporal transferability have not been determined. This paper deals with one updating method—the use of disaggregate data to update alternative-specific constants—and investigates the factors affecting the temporal transferability of the updated constants. In the analysis, repeated cross-section data collected in the Chukyo metropolitan area are divided, efficiently generating many application areas. The analysis showed that the factors can depend on regional characteristics and past travel behaviours (inertia), and are anti-symmetric and path-dependent of changes in the level of service.     

Mode choice models' ability to express intention to change travel behaviour considering non-compensatory rules and latent variables

Disaggregate behaviour choice models have been improved in many aspects, but they are rarely evaluated from the viewpoint of their ability to express intention to change travel behaviour. This study compared various models, including objective and latent models and compensatory and non-compensatory decision-making models. Latent models contain latent factors calculated using the LISREL (linear structural relations) model. Non-compensatory models are based on a lexicographic-semiorder heuristic. This paper proposes ‘probability increment’ and ‘joint probability increment’ as indicators for evaluating the ability of these models to express intention to change travel behaviour. The application to commuting travel data in the Chukyo metropolitan area in Japan showed that the appropriate non-compensatory and latent models outperform other models.     

Correcting state dependence and serial correlation in the RP/SP combined estimation method

Revealed preference (RP) data and stated preference (SP) data have complementary characteristics for model estimation. To enhance the advantages of both data types, a combined estimation method is proposed. This paper discusses the method and practical considerations in applying it, and introduces a new method of considering serial correlation of RP and SP data. An empirical analysis is also presented.     

Home charge timing choice behaviors of plug-in hybrid electric vehicle users under a dynamic electricity pricing scheme

This paper examines choice behaviors pertaining to the time at which users of plug-in hybrid electric vehicle with 24 km electric range charge their vehicles after arriving at home under a dynamic electricity pricing scheme. The following mutually exclusive alternatives are presented: no charging, charging immediately after arriving at home, charging at the cheapest time, and charging at other times. Four versions of a mixed logit model with unobserved heterogeneity are applied to panel data on vehicle usage from 9 households with 2226 observations in Toyota City. Estimation results suggest that users’ willingness to charge become stronger with increasing driving distance when the driving distance is less than the electric range of 24 km, while tend not to charge when the driving distance is longer than the electric range. Users who return home at the cheapest time or during the day are willing to charge immediately after arriving at home. Electricity prices significantly affect choices to charge at the cheapest time for all users, and stay-at-home mother users and users returning home in the evening tend to charge at the cheapest time. Users returning home in the evening also tend to charge at other times, and being accustomed to charge at a certain time increases the probability of charging at other times. In addition, considerable variations are found across individuals with respect to their preferences for charge timing alternatives as well as for electricity prices.     

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.