Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

Equilibria of bilateral taxi–customer searching and meeting on networks

This paper proposes an equilibrium model to characterize the bilateral searching and meeting between customers and taxis on road networks. A taxi driver searches or waits for a customer by considering both the expected searching or waiting time cost and ride revenue, and a customer seeks a taxi ride to minimize full trip price. We suppose that the bilateral taxi–customer searching and meeting occurs anywhere in residential and commercial zones or at prescribed taxi stands, such as an airport or a railway station. We propose a meeting function to spell out the search and meeting frictions that arise endogenously as a result of the distinct spatial feature of the area and the taxi–customer moving decisions. With the proposed meeting function and the assumptions underlying taxi–customer search behaviors, the stationary competitive equilibrium achieved at fixed fare prices is determined when the demand of the customers matches the supply of taxis or there is market clearing at the prevailing searching and waiting times in every meeting location. We establish the existence of such an equilibrium by virtue of Brouwer’s fixed-point theorem and demonstrate its principal operational characteristics with a numerical example.     

Evaluating locational accessibility to the US air transportation system

Although there are hundreds of airports that support commercial air passenger traffic in the United States (US), not all areas are equivalently served by the commercial air transportation system. Locations in the US differ with respect to their level of access to the commercial air network and their overall accessibility within the system. Given the complexity of the domestic commercial air passenger network and supporting infrastructure, past research has only been able to provide a limited assessment of locational accessibility within the United States. To address these complexities, this paper proposes a new metric that incorporates measures of access to air transport as well as accessibility within air transportation networks. Using a comprehensive dataset on scheduled airline service, the developed approach is then applied to the US domestic commercial passenger air transportation network to explore geographic differentials in accessibility. Results suggest marked differences between core-based statistical areas throughout the US.     

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.     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Transit assignment under crowded conditions

This paper presents a transit assignment algorithm for crowded networks. Both congestion in vehicles and queuing at stations are explicitly taken into account in predicting passenger flows for a fixed pattern of origin-destination trip demands. The overflow effects due to insufficient capacity of transit lines are considered to be concentrated at transit stations, while the in-vehicle congestion effects (or discomforts) are considered to be dependent on in-vehicle passenger volume. Overflow delay at a transit station is dependent on the number of excess passengers required to wait for the next transit car. We use a logit model to determine the split between passengers that chose to wait for the next transit car and passengers that chose to board on the alternative transit lines. The proposed algorithm predicts how passenger will choose their optimal routes under both queuing and crowded conditions.     

Managing passenger behavioral intention: an integrated framework for service quality,satisfaction, perceived value,and switching barriers

This paper seeks to improve our understanding of passengers’ behavioral intention by proposing an integrated framework from the attitudinal perspective. According to the literature in marketing research, we establish a causal relationship model that considers “service quality-satisfaction-behavioral intentions” paradigm, perceived value theory, and switching barrier theory. Exploring passengers’ behavioral intention from satisfaction and perceived value help to understand how passengers are attracted by the company, while switching barriers assist in realizing how passengers are “locked” into a relationship with the current company. Furthermore, in order to capture the nature of service quality, we adopt a hierarchical factor structure which serves service quality as the higher-order factor. In this study, coach industry is selected as our research subject. The empirical results, as hypothesized, show that all causal relationships are statistically significant, and perceived value us the most important predictor of satisfaction and passengers’ behavioral intention. In conclusion, the managerial implications and suggestions for future research are discussed.     

A frequency-based maritime container assignment model

This paper transfers the classic frequency-based transit assignment method of Spiess and Florian to containers demonstrating its promise as the basis for a global maritime container assignment model. In this model, containers are carried by shipping lines operating strings (or port rotations) with given service frequencies. An origin–destination matrix of full containers is assigned to these strings to minimize sailing time plus container dwell time at the origin port and any intermediate transhipment ports. This necessitated two significant model extensions. The first involves the repositioning of empty containers so that a net outflow of full containers from any port is balanced by a net inflow of empty containers, and vice versa. As with full containers, empty containers are repositioned to minimize the sum of sailing and dwell time, with a facility to discount the dwell time of empty containers in recognition of the absence of inventory. The second involves the inclusion of an upper limit to the maximum number of container moves per unit time at any port. The dual variable for this constraint provides a shadow price, or surcharge, for loading or unloading a container at a congested port. Insight into the interpretation of the dual variables is given by proposition and proof. Model behaviour is illustrated by a simple numerical example. The paper concludes by considering the next steps toward realising a container assignment model that can, amongst other things, support the assessment of supply chain vulnerability to maritime disruptions.     

A single-period analysis of a two-echelon inventory system with dependent supply uncertainty

Disruptions and random supplies have been important sources of uncertainty that should be considered in the design and control of supply chains. There have been many real world examples in which a single catastrophic event has simultaneously degraded the capabilities of several suppliers leading to considerable erosion of profits and goodwill for a company. However, the literature on analytical models that account for the dependence nature of disruptions and its impact on supply chain performance is sparse.In this paper, we consider an m-manufacturer, 1-retailer, newsvendor inventory system with stochastically dependent manufacturing capacities, caused by random disruptions that may simultaneously inflict damages to the capacities of the manufacturers. We develop the structural/analytical properties of key performance measures and optimal inventory policies for the multi-source and assembly inventory systems. We show that stochastic dependence in disruptions can have opposite effects on system performance in the multi-source and assembly systems. While risk diversification is preferred in the multi-source system, risk concentration is preferred in the assembly system. Our results also suggest that, if the retailer ignores the effect of dependent disruptions, then in the multi-source structure, it would underestimate the cost, overestimate the fill rate, and order more units than the optimum; however, in the assembly structure, the opposite would happen. We perform a comprehensive numerical study to validate our analytical results and generate useful managerial and operational insights for effective risk management of supply chains in the presence of dependent supply uncertainty.     

The cost saving potential of carsharing in a US context

Carsharing is a vehicle sharing service for those with occasional need of private transportation. Transportation planners are beginning to see great potential for carsharing in helping to create a more diversified and sustainable transport system. While it has grown quickly in the US in recent years, it is still far from the level where it can deliver significant aggregate benefits. A key element to the potential growth of carsharing is its ability to provide cost savings to those who adopt it in favor of vehicle ownership. This research seeks to quantify these potential cost savings. The costs of carsharing and vehicle ownership are compared based on actual vehicle usage patterns from a large survey of San Francisco Bay Area residents. The results of this analysis show that a significant minority of Bay Area households own a vehicle with a usage pattern that carsharing could accommodate at a lower cost. Further research is required to indentify how these cost savings translate to the adoption of carsharing.