Level Of Service Attributes Research Articles

To switch travel mode or not? Impact of Smartphone delivered high‐quality multimodal information

Travellers' mode switch behaviour with the presence of high-quality Smartphone delivered multimodal information (SMMI) seems to have rarely been addressed. This study investigated commuters’ en-trip mode decision about switching from ‘auto’ to ‘park-and-ride’ (P + R) under high-quality SMMI that provides travel time for both modes, delay for auto, cause of delay, P + R cost and comfort level of rail transit. It is based on a stated preference survey of Shanghai travellers. A binary logit model was developed to identify contributing factors that affect mode switching decisions. Results showed that SMMI can significantly influence mode choice and its impacts depend on traveller attributes, driver's previous experience, and level of service attributes. Statistically significant explanatory variables in the model are delay for auto, comfort level of rail transit, gender, education level, income, driving experience, driving frequency, main criterion of mode choice, owning an easy public transportation ride card, previous use of P + R, perceived value of existing real-time traveller information and frequency of using real-time traveller information. This study also developed a practical logit model that encompasses policy related explanatory variables to obtain policy implications for real application of SMMI services in Shanghai.

The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice

The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.

Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.

Forecasting Cybercar Use for Airport Ground Access: Case Study at Baltimore Washington International Airport

Nested logit models calibrated on revealed preference (RP) and stated preference (SP) data have been used in this paper to study the potential market of a cybernetic transportation system for airport ground access. This innovative concept of transit mode could complement mass transit and nonmotorized transport modes, providing passenger service for any location at any time. Our analysis is based on a data set collected at Baltimore Washington Thurgood Marshall International (BWI) airport; the proposed cybercar service was designed to connect the Greenbelt metro station to the airport. The explanatory variables used in the utility functions of the RP/SP joint models are level of service attributes, characteristic specific to the new service offered, and socioeconomic variables. The estimated models are used to predict the market share of automobile, taxi, cybercar, and modified transit. In addition, elasticity tests are performed in order to study individuals’ sensitivity to the change of travel time and t...

Nonlinearity of Response to Level-of-Service Variables in Travel Mode Choice Models

It is important to accommodate variations in responsiveness (or response heterogeneity) to level-of-service (LOS) attributes in travel mode choice models. This response heterogeneity may be disaggregated into a systematic (observed) component and a random (unobserved) component. Earlier studies typically considered systematic response heterogeneity by examining differences in LOS response sensitivities caused by individual demographic and other attributes. The emphasis of the presented research is on another element of systematic response heterogeneity: that originating from nonlinear responsiveness to LOS attributes. Specifically, both the components of systematic response heterogeneity (due to individual characteristics and nonlinear responsiveness) as well as unobserved response heterogeneity are considered at the same time, and the empirical results of models that assume a traditional linear responsiveness to LOS attributes are compared with those that adopt a nonlinear responsiveness to LOS attributes. The empirical analysis uses data from the Austin commuter stated preference survey to examine commute travel mode choice. The nonlinear specifications for travel time and travel time unreliability indicate that in the first 15 min commuters place little value on travel time and high value on travel time reliability. Beyond 15 min, however, the valuation of travel time increases rapidly, whereas that of travel time reliability drops dramatically. In addition to clearly indicating the nonlinear nature of responsiveness to travel time and travel time unreliability, the results indicate that ignoring nonlinear responsiveness leads, in the current empirical context, to ( a) biased parameter estimates, ( b) an inflated estimate of unobserved heterogeneity, ( c) counterintuitive signs on the LOS variables for a high fraction of individuals, ( d) inaccurate estimates of willingness-to-pay measures, and ( e) loss in model fit.

Nonlinearity of Response to Level-of-Service Variables in Travel Mode Choice Models

It is important to accommodate variations in responsiveness (or response heterogeneity) to level-of-service (LOS) attributes in travel mode choice models. This response heterogeneity may be disaggregated into a systematic (observed) component and a random (unobserved) component. Earlier studies typically considered systematic response heterogeneity by examining differences in LOS response sensitivities caused by individual demographic and other attributes. The emphasis of the presented research is on another element of systematic response heterogeneity: that originating from nonlinear responsiveness to LOS attributes. Specifically, both the components of systematic response heterogeneity (due to individual characteristics and nonlinear responsiveness) as well as unobserved response heterogeneity are considered at the same time, and the empirical results of models that assume a traditional linear responsiveness to LOS attributes are compared with those that adopt a nonlinear responsiveness to LOS attributes. The empirical analysis uses data from the Austin commuter stated preference survey to examine commute travel mode choice. The nonlinear specifications for travel time and travel time unreliability indicate that in the first 15 min commuters place little value on travel time and high value on travel time reliability. Beyond 15 min, however, the valuation of travel time increases rapidly, whereas that of travel time reliability drops dramatically. In addition to clearly indicating the nonlinear nature of responsiveness to travel time and travel time unreliability, the results indicate that ignoring nonlinear responsiveness leads, in the current empirical context, to (a) biased parameter estimates, (b) an inflated estimate of unobserved heterogeneity, (c) counterintuitive signs on the LOS variables for a high fraction of individuals, (d) inaccurate estimates of willingness-to-pay measures, and (e) loss in model fit.

Sensitivity of Level of Service in Shorthaul Market

Current transportation planning requires a better understanding of the mode choice behavior of intercity passengers in the short-haul market. Considerable work has been completed on the topic for the medium and long-haul ranges, but a limited body of literature is addressed to the problem relative to the short-haul market. In this study, an attempt has been made to construct behavioral models incorporating modal level of service attributes. An aggregate and a disaggregate mode choice model are formulated based on the logit function. Using linear and nonlinear regression for the calibration of the aggregate model, and the maximum likelihood estimation method for the disaggregate model, the formulations are tested for reliability and validity by comparing them with other published research findings. The model specification is found to be acceptable. For the data base used in this research, the log-linear model produced better results. The method has been used to analyze the desirable strategy of local air services and to evaluate the sensitivity of the automobile user to highway conditons and highway tolls.