Commuting costs and the residential mobility of job changers

In traffic and transport research, attention is given to the relevance of location patterns of activities to moving behaviour, the inverse causality being mostly left out of account. This paper considers what influence (changes in) travel costs have on moving behaviour and residential choice. The analysis has been carried out for employed people who change jobs. The residential choice has been split into a marginal probability of moving and a conditional destination choice. Both choices appear to be influenced significantly by travel‐cost variables.     

An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction

The French government has implemented a periodical vehicle inspection program, which aims at maintaining proper functioning of the vehicle and ensuring the emissions control systems installed on the vehicle work properly. Also, an incentive program for scrapping old vehicles was introduced in 1994 through 1996 to promote the replacement of those vehicles with higher emissions by newer vehicles with lower emissions. A hazard-based duration model of household vehicle transaction behavior has been developed in this study to examine the effects of the inspection program and the grant for scrappage on vehicle transaction timing. The model is developed as a competing risks model assuming the following three types of competing risks: replacing one of the vehicles in the household fleet, disposing of one vehicle in the fleet, and acquiring one vehicle to add to the fleet. The empirical analysis is carried out using the panel data of French households' vehicle ownership from 1984 to 1998, obtained by the panel survey called Parc-Auto, which has been conducted by a French marketing firm, SOFRES, since 1976. The long panel observation period facilitates the introduction of macro-economic indicators into the model, enabling the analysis to distinguish the effects of policy measures from macro-economic factors. The empirical results indicate that the expected vehicle holding duration becomes 1.3 years longer under the inspection program than before the program commenced, given that the vehicle is replaced by another vehicle at the end of the holding duration; and that the conditional probability of replacing a vehicle aged 10 years and over becomes 1.2 times higher, and the average holding duration becomes shorter by 3.3 years, when the grant for scrappage is available.     

如何办理机动车登记、变更、抵押登记手续?

问题一:如何办理机动车的登记? 答:初次申领号牌、行驶证的,除国家机动车产品主管部门认定免予检验的车型外,应当在申请注册登记前,到机动车安全技术检验机构对机动车进行安全技术检验,取得安全技术检验合格证明.     

The role of habit and residential location in travel behavior change programs,a field experiment

Transportation - Early evaluations of travel change programs demonstrated dramatic success in shifting people out of cars and into transit and active travel. Yet methodological shortcomings of...     

A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices

Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However, such models need to take into account self-selection effects in residential location choice, wherein households choose to reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon, well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copula-based framework that facilitates the estimation of joint equations systems with error dependence structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency among the choice dimensions and that self-selection effects cannot be ignored when modeling land use-travel behavior interactions.     

A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density

This paper develops a new method to solve multivariate discrete–continuous problems and applies the model to measure the influence of residential density on households’ vehicle fuel efficiency and usage choices. Traditional discrete–continuous modelling of vehicle holding choice and vehicle usage becomes unwieldy with large numbers of vehicles and vehicle categories. I propose a more flexible method of modelling vehicle holdings in terms of number of vehicles in each category, using a Bayesian multivariate ordinal response system. I also combine the multivariate ordered equations with Tobit equations to jointly estimate vehicle type/usage demand in a reduced form, offering a simpler alternative to the traditional discrete/continuous analysis. Using the 2001 National Household Travel Survey data, I find that increasing residential density reduces households’ truck holdings and utilization in a statistically significant but economically insignificant way. The results are broadly consistent with those from a model derived from random utility maximization. The method developed above can be applied to other discrete–continuous problems.     

Modeling residential location choice in relation to housing location and road tolls on congested urban highway networks

We present a reformulation of the residential location submodel of the Integrated Model of Residential and Employment Location as a network equilibrium problem, thereby making travel costs by auto endogenous. The location of housing supply is examined as a welfare maximization problem for both user-optimal and system-optimal travel costs using concepts of bilevel programming. Finally, we briefly discuss how the employment submodel can be reformulated, and the entire model solved as a variational inequality problem.     

Exploring the relationship between vehicle safety and fuel efficiency in automotive design

Panel data analysis is used within a fixed effect model to examine the relationship between vehicle safety ratings and fuel efficiency of 45 new vehicle models sold in the US between 2002 and 2007. While conventional wisdom and most early literature suggest that lighter, more fuel efficient vehicles are less safe to their occupants, the tests show a positive relationship between vehicle safety ratings and fuel efficiencies not only within and across most size classes but also for vehicles produced by both the US and Asian automakers. We also explore the design initiatives by manufacturers to compensate for the reductions in weight/size of fuel-efficient vehicles.     

Modeling the interaction between buses,passengers and cars on a bus route using a multi-agent system

This paper models part of a public transport network (PTN), specifically, a bus route, as a small-size multi-agent system (MAS). The proposed approach is applied to a case study considering a ‘real world’ bus line within the PTN in Auckland, New Zealand. The MAS-based analysis uses modeling and simulation to examine the characteristics of the observed system – autonomous agents interacting with one another – under different scenarios, considering bus capacity and frequency of service for existing and projected public transport (PT) demand. A simulation model of a bus route is developed, calibrated and validated. Several results are attained, such as when the PT passenger load is not close to bus capacity, this load has no effect on average passenger waiting time at bus stops. The model proposed can be useful to practitioners as a tool to model the interaction between buses and other agents.     

Household Mobility Tool Ownership: Modeling Interactions between Cars and Season Tickets

This paper moves beyond traditional models of car ownership in that we propose a framework for modeling household-level decisions to acquire specific types and numbers of mobility tools to fulfill the mobility needs of household members. The framework is applied to a data set collected during the winter and spring of 2000/2001 in the German city Karlsruhe via an interactive web-based stated response survey in which respondents could optimize their household mobility tool sets through on-line feedback concerning the estimated costs of the sets. In our analysis, bivariate ordered probit models are estimated for three combinations of mobility tools: season tickets (i.e., transit passes) and cars, season tickets and small cars and season tickets and large cars. In all instances, strong substitution effects are found – that is, as the number of season tickets increases, the number of cars decreases. This finding underscores the need to move beyond simple models of car ownership to comprehensive models of mobility tool ownership. As demonstrated by our research, failure to do so is likely to lead to biased results.     

Congestion and emissions mitigation: A comparison of capacity,demand, and vehicle based strategies

Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO₂e, CO, and NO_x through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM_2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation.     

Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

Exploring the connections among residential location,self-selection,and driving: Propensity score matching with multiple treatments

A large number of studies have investigated the association between the built environment and travel behavior. However, most studies did not explicitly quantify the contribution of residential self-selection to the connection. Using the 2006 data collected from a regional travel diary in Raleigh, NC, this study applies propensity score matching to explore the effects of the regional location of individuals’ residences on their vehicle miles driven. We found that residential location plays a more important role in affecting driving behavior than residential self-selection; and that the self-selection effect is non-trivial when we compare driving behavior between urban residents and people living in other areas. Therefore, for such comparisons, the observed influence of residential locations on driving should be appropriately discounted when we evaluate the causal impacts of the built environment on travel behavior.     

Real-time parking information management to reduce search time,vehicle displacement and emissions

The time spent searching for free parking spaces can produce considerable environmental pollution. Information on parking availability can be a powerful instrument for reducing these search costs. This paper develops a demand assignment model to evaluate the benefits of manipulating information with the objective of reducing the time and distances involved in finding a parking-place; including the walking distances involved. Using the full search procedure it was found that improvements of some 10% in efficiency could be achieved, but only at high computational costs. A genetic algorithm was programmed that increases the possibility of finding the optimum information conditions that can be translated into lower emissions of toxic greenhouse gases.     

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

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

     

Reducing transit fleet emissions through vehicle retrofits,replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO₂ equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.     

Modeling the structural relationships among short-distance travel amounts,perceptions, affections,and desires

Using structural equation modeling, the relationships among travel amounts, perceptions, affections, and desires across five short-distance (one-way trips of less than 100 miles) travel categories (overall, commute, work/school-related, entertainment/social/recreation, and personal vehicle) are examined. The models are estimated using data collected in 1998 from more than 1300 working commuters in the San Francisco Bay Area. A cross-model analysis reveals three robust relationships, namely: (1) myriad measures of travel amounts work together to affect perceptions; (2) perceptions are consistently important in shaping desires; and (3) affections have a positive relationship with desires. The second finding suggests that two individuals who travel the same objective amount may not have the same desire to reduce their travel: how much individuals perceive their travel to be is important. The third point argues that the degree to which travel is enjoyed is a key determinant of shaping desires to reduce travel: the more travel is enjoyed, the less the desire to reduce it.