Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling

When a new public transport service is introduced it would be valuable for public authorities, financing organisations and transport operators to know how long it will take for people to start to use the service and what factors influence this. This paper presents results from research analysing the time taken for residents living close to a new guided bus service to start to use (or adopt) the service. Data was obtained from a sample of residents on whether they used the new service and the number of weeks after the service was introduced before they first used it. Duration modelling has been used to analyse how the likelihood of starting to use the new service changes over time (after the introduction of the service) and to examine what factors influence this. It is found that residents who have not used the new service are increasingly unlikely to use it as time passes. Those residents gaining greater accessibility benefits from the new service are found to be quicker to use the service, although the size of this effect is modest compared to that of other between-resident differences. Allowance for the possibility that there existed a proportion of the sample that would never use the new service was tested using a split population model (SPD) model. The SPD model indicates that 36% of residents will never use the new service and is informative in differentiating factors that influence whether Route 20 is used and when it is used.

Modelling the dynamics of bus use in a changing travel environment using panel data

Panel data offers the potential to represent the influence on travel choices of changing circumstances, past history and persistent individual differences (unobserved heterogeneity). A four-wave panel survey collected data on the travel choices of residents before and after the introduction of a new bus rapid transit service. The data shows gradual changes to bus use over the four waves, implying time was required for residents to become aware of the new service and to adapt to it. Ordered response models are estimated for bus use over the survey period. The results show that the influence of level of service (LOS) is underestimated if unobserved heterogeneity is not taken into account. The delayed response to the new service is able to be well represented by including LOS as a lagged variable. Current bus use is found to be conditioned on past bus use, but with additional influence of lagged LOS and unobserved heterogeneity. It is shown how different model specifications generate different evolution patterns with the most realistic predictions arising from a model which takes into account lagged responses to change in LOS and unobserved heterogeneity. The paper demonstrates the feasibility of developing panel data models that can be applied to forecasting the effect of interventions in the travel environment. Longer panels—encompassing periods of both stability and change—are required to support future efforts at modelling travel choice dynamics.     

Numerical Investigation of Sloshing Under Roll Excitation at Shallow Liquid Depths and the Effect of Baffles

Sloshing is relevant in several applications like ship tanks, space and automotive industry and seiching in harbours. Due to the relationship between ship and s...     

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

Transportation - This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet...     

Effectiveness of using variable message signs to disseminate dynamic traffic information: Evidence from field trails in European cities

Variable message signs (VMS) can provide up‐to‐date traffic information and guidance to drivers through electronic signs at the roadside. The paper draws together the results from VMS field trials conducted in nine cities as part of European Union‐sponsored research projects carried out between 1994 and 1999. The projects followed common guidelines in carrying out field trial evaluations, which has enabled generalized findings to be made on the impacts of the different VMS applications. The main emphasis in the paper is on drivers' reactions to VMS and the impacts of VMS on road network efficiency. Results are reported for four different types of traffic information. For incident messages, it is not only the severity of the problem reported that influences the level of diversions, but also other factors such as the specific location mentioned and the availability of viable alternative routes to avoid the problem location. For route guidance information, it is demonstrated that substantial diversions occur when the route advice differs from that given normally. For continuous information describing the traffic state on a major route, information increases the use of the major route and reduces use of alternative routes if there are no traffic problems reported on the major route. Travel time information was well regarded by drivers and found to be effective in inducing route changes. In general, the deployments of VMS to inform drivers of traffic conditions have proved successful in terms of improving network travel times and reducing environmental impacts. Whilst such changes have been relatively small, driver perception of the benefits is much higher. This is potentially very significant in terms of the role that VMS can play in the development of integrated transport strategies, as the provision of information may encourage greater acceptance of a range of demand management measures.     

How commuting affects subjective wellbeing

Transportation - Commuting between home and work is routinely performed by workers and any wellbeing impacts of commuting will consequently affect a large proportion of the population. This paper...     

Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method

Transportation - Large scale activity-based simulation models inform a variety of transportation and planning policies using models that often rely on fixed or flexible workplace location in a...     

A statistical process control approach using cumulative sum control chart analysis for traffic data quality verification and sensor calibration for weigh-in-motion systems

Weigh-in-motion systems have been widely used by state agencies to collect the traffic data on major state roadways and bridges to support traffic load forecasting, pavement design and analysis, infrastructure investment decision making, and transportation planning. However, the weigh-in-motion system itself poses difficulties in obtaining accurate data due to sensor characteristics that can be sensitive to vehicle speed, weather conditions, and changes in surrounding pavement conditions. This study focuses on developing a systematic methodology to detect weigh-in-motion sensor bias and enhance current practices for weigh-in-motion calibration. A mixture modeling technique using an expectation maximization algorithm was developed to divide the vehicle class 9 gross vehicle weight into three normally distributed components: unloaded, partially loaded, and fully loaded trucks. Then the well-known statistical process control technique cumulative sum control chart analysis was applied to expectation maximization estimates of daily mean gross vehicle weight for fully loaded trucks to identify and estimate shifts in the weigh-in-motion sensor. Special attention was given to the presence of autocorrelation in the data by fitting an autoregressive time-series model and then performing cumulative sum control chart analysis on the fitted residuals. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the weigh-in-motion sensor accurately and also indicate the time point when the system went out of calibration. This methodology can be effectively implemented by state agencies, resulting in more accurate and reliable weigh-in-motion data.     

A structural equations analysis of commuters' activity and travel patterns

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, DC metropolitan area to investigate and estimate relationships among socio-demographics, activity participation, and travel behavior. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information. Three types of structural equations model systems were estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third that models the generation of complex work trip chains. The model estimation results show that strong relationships do exist among commuters' socio-demographic characteristics, activity engagement information, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. Virtually all of the results obtained in this paper corroborate earlier findings reported in the literature regarding relationships among time use, activity participation, and travel. This revised version was published online in June 2006 with corrections to the Cover Date.