On the cost of misperceived travel time variability

Recent studies show that traveler’s scheduling preferences compose a willingness-to-pay function directly corresponding to aggregate measurement of travel time variability under some assumptions. This property makes valuation on travel time variability transferable from context to context, which is ideal for extensive policy evaluation. However, if respondents do not exactly maximizing expected utility as assumed, such transferability might not hold because two types of potential errors: (i) scheduling preference elicited from stated preference experiment involving risk might be biased due to misspecification and (ii) ignoring the cost of misperceiving travel time distribution might result in undervaluation. To find out to what extent these errors matter, we reformulate a general scheduling model under rank-dependent utility theory, and derive reduced-form expected cost functions of choosing suboptimal departure time under two special cases. We estimate these two models and calculate the empirical cost due to misperceived travel time variability. We find that (i) travelers are mostly pessimistic and thus tend to choose departure time too earlier to bring optimal cost, (ii) scheduling preference elicited from stated choice method could be quite biased if probability weight- ing is not considered and (iii) the extra cost of misperceiving travel time distribution contributes trivial amount to the discrepancy between scheduling model and its reduced form

The impact of reliable range estimation on battery electric vehicle feasibility

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range or when their journeys were unexpectedly extended. This paper quantifies the impact of reliable range estimation on BEV feasibility using GPS-tracked travel survey data, collected over an 18-month period (from November 2004 to April 2006) in the Seattle metropolitan area. BEV feasibility is quantified as the number of days when travel adaption is needed if a driver replaces a conventional gasoline vehicle (CGV) with a BEV. The distribution of BEV range is estimated based on the real-world fuel efficiency data. A driver is assumed to choose between using a BEV or a substitute gasoline vehicle, based on the cumulative prospect theory (CPT). BEV is considered feasible for a particular driver if he/she needs to use a substitute vehicle on less than 0.5% of the travel days. By varying the values of some CPT parameter, the percentage of BEV feasible vehicles could change from less than 5% to 25%. The numerical results also show that with a 50% reduction in the standard deviation and 50% increase in the mean of the BEV range distribution BEV feasibility increases from less than 5% of the sampled drivers to 30%

A two-stage approach to ridesharing assignment and auction in a crowdsourcing collaborative transportation platform.

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open requests at any given time can be large. On the other hand, service providers or drivers often evaluate the first couple of pending requests in deciding which request to participate in. This kind of behavior made by the driver may have potential detrimental implications for all parties involved. First, the drivers typically end up participating in those requests that require longer driving distance for higher profit. Second, the passengers tend to overpay under a competition free environment compared to the situation where the drivers are competing with each other. Lastly, when the drivers and passengers are not satisfied with their outcomes, they may leave the platforms. Therefore the platform could lose revenues in the short term and market share in the long term. In order to address these concerns, a decision-making support procedure is needed to: (i) provide recommendations for drivers to identify the most preferable requests, (ii) offer reasonable rates to passengers without hurting driver’s profit. This dissertation proposes a mathematical modeling approach to address two aspects of the crowdsourcing ridesharing platform. One is of interest to the centralized platform management on the assignment of requests to drivers; and this is done through a multi-criterion many to many assignment optimization. The other is of interest to the decentralized individual drivers on making optimal bid for multiple assigned requests; and this is done through the use of prospect theory. To further validate our proposed collaborative transportation framework, we analyze the taxi yellow cab data collected from New York city in 2017 in both demand and supply perspective. We attempt to examine and understand the collected data to predict Uber-like ridesharing trip demands and driver supplies in order to use these information to the subsequent multi-criterion driver-to-passenger assignment model and driver\u27s prospect maximization model. Particularly regression and time series techniques are used to develop the forecasting models so that centralized module in the platform can predict the ridesharing demands and supply within certain census tracts at a given hour. There are several future research directions along the research stream in this dissertation. First, one could investigate to extend the models to the emerging concept of Physical Internet on commodity and goods transportation under the interconnected crowdsourcing platform. In other words, integrate crowdsourcing in prevalent supply chain logistics and transportation. Second, it\u27s interesting to study the effect of Uber-like crowdsourcing transportation platforms on existing traffic flows at the various levels (e.g., urban and regional)

Gamification in transport interventions: Another way to improve travel behavioural change

Gamification is dramatically transforming how behaviour change interventions are delivered. The design of gaming products in the field of transport, a field which is perceived as having derived demand, is largely underdeveloped. This paper explores gamification in the context of transport, proposes a conceptual theoretical framework that explains why and how gamification may be designed and evaluated, and synthesises current practice regarding the range of interventions offered thus far. The conclusions identify strategies and implications for the improvement to existing schemes as well as guidance for future research into gamification

An Assessment of Prospect Theory in Tourism Decision-Making Research

Prospect theory has been an essential theoretical foundation for behavioral economics, as recognized with the Nobel Prize in economic sciences in 2002. The growing interest in behavioral economics among tourism researchers necessitates a systematic assessment of prospect theory and its application in tourism research to critically examine the current status of tourism decision-making studies. This study therefore clarifies the theoretical background of prospect theory and analyzes 93 published studies to examine how prospect theory has performed in explaining tourism decision-making. The study also evaluates the application of prospect theory in tourism research and provides future research directions with respect to under-researched dimensions, reference points, dynamic decision-making processes, and the logical continuity and systemization of prospect theory

Modelling Train Station Choice under Uncertainty for Park and Ride Users

This research develops a novel theoretical framework for modelling train station choice under uncertainty for park and ride users. Three uncertain factors, travel time to station, parking search time and crowding on trains, are modelled to estimate station choice probabilities, the risk attitudes of respondents and the preference heterogeneity of individuals. This study may support planning decisions on the location, price and capacity of P&R facilities, and provide evidence for evaluating P&R investment decisions

On the cost of misperceived travel time variability

Recent studies show that traveler’s scheduling preferences compose a willingness-to-pay function directly corresponding to aggregate measurement of travel time variability under some assumptions. This property makes valuation on travel time variability transferable from context to context, which is ideal for extensive policy evaluation. However, if respondents do not exactly maximizing expected utility as assumed, such transferability might not hold because two types of potential errors: (i) scheduling preference elicited from stated preference experiment involving risk might be biased due to misspecification and (ii) ignoring the cost of misperceiving travel time distribution might result in undervaluation. To find out to what extent these errors matter, we reformulate a general scheduling model under rank-dependent utility theory, and derive reduced-form expected cost functions of choosing suboptimal departure time under two special cases. We estimate these two models and calculate the empirical cost due to misperceived travel time variability. We find that (i) travelers are mostly pessimistic and thus tend to choose departure time too earlier to bring optimal cost, (ii) scheduling preference elicited from stated choice method could be quite biased if probability weight- ing is not considered and (iii) the extra cost of misperceiving travel time distribution contributes trivial amount to the discrepancy between scheduling model and its reduced form