Search

Freight transportation network is an essential backbone for supporting the industrial activities and economic developments of the nation and global trade. In this paper, we extend the network-based measures recently developed by Xu et al. (2018) for assessing freight transportation network redundancy – an important component in making freight transportation networks more robust and resilient against disruptions. Redundancy of freight transportation networks is characterized by two network-based measures. The route diversity measure evaluates the existence of multiple efficient routes available for freight users or the degree of connections between a specific origin-destination (O-D) pair, while the network spare capacity measure quantifies the network-wide spare capacity with an explicit consideration of congestion effect and modal split. These two measures can complement each other by providing a two-dimensional characterization of freight transportation network redundancy that can be used to provide information to freight users as well as to assist network planners in making future infrastructure investment decisions to enhance the redundancy of freight transportation networks. A case study of the Utah freight network for transporting coal is provided to demonstrate the features of the two network-based redundancy measures as well as the applicability of assessing the redundancy of freight transportation networks. The analyses reveal that the two measures provide useful results from two perspectives that may be of benefit to freight carriers and transportation system planners. [ABSTRACT FROM AUTHOR]

• A multi-modal network equilibrium problem with captive mode travellers. • An equivalent mathematical programming formulation admits unique solution. • A consistent proportional synthetic index for evaluating transport policies. • Mode captivity significantly effects the odds of making different EBL expansion plans. • We implement the model in a real-size multi-modal network. In this paper, we consider captive mode travelers (those who have no other choices but rely on one specific travel mode for daily commuting trips) in a multi-modal network equilibrium (MMNE) problem. Specifically, the dogit model is adopted to account for captive mode travelers in the modal split problem, and the path-size logit (PSL) model is used to capture route overlapping effects in the traffic assignment problem. The dogit-PSL MMNE model is formulated as an equivalent entropy-based mathematical programming (MP) problem, which admits solution existence and uniqueness. Three numerical examples are provided. The first example examines the effects of mode captivity and route overlapping on network performances and observes that accounting for captive mode travelers would produce different equilibrium states and hence the network performance indicators. The second example applies the dogit-PSL MMNE model for evaluating the exclusive bus lane (EBL) expansion plans, in which a consistent synthetic proportional index is proposed. Numerical results show that considering mode captivity may produce substantial impacts on the odds (up to 50 percent of odds in the given scenarios) of making different EBL line expansion decisions. The third example implements the dogit-PSL MMNE model in the Seoul network to show the applicability of the dogit-PSL MMNE model in a real-size multi-modal system. [ABSTRACT FROM AUTHOR]

Recent empirical studies have found widespread inaccuracies in traffic forecasts despite the fact that travel demand forecasting models have been significantly improved over the past few decades. We suspect that an intrinsic selection bias may exist in the competitive project appraisal process, in addition to the many other factors that contribute to inaccurate traffic forecasts. In this paper, we examine the potential for selection bias in the governmental process of Build-Operate-Transfer (BOT) transportation project appraisals. Although the simultaneous consideration of multiple criteria is typically used in practice, traffic flow estimate is usually a key criterion in these appraisals. For the purposes of this paper, we focus on the selection bias associated with the highest flow estimate criterion. We develop two approaches to quantify the level and chance of inaccuracy caused by selection bias: the expected value approach and the probability approach. The expected value approach addresses the question “ to what extent is inaccuracy caused by selection bias? ”. The probability approach addresses the question “ what is the chance of inaccuracy due to selection bias? ”. The results of this analysis confirm the existence of selection bias when a government uses the highest traffic forecast estimate as the priority criterion for BOT project selection. In addition, we offer some insights into the relationship between the extent/chance of inaccuracy and other related factors. We do not argue that selection bias is the only reason for inaccurate traffic forecasts in BOT projects; however, it does appear that it could be an intrinsic factor worthy of further attention and investigation. [ABSTRACT FROM AUTHOR]

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources. [ABSTRACT FROM AUTHOR]

Abstract: Flexibility of the transportation system is one of the important performance measures needed to deal with demand changes. In this paper, we provide a quantitative assessment of capacity flexibility for the passenger transportation network using bi-level network capacity models. Two approaches for assessing the value of capacity flexibility are proposed. One approach is based on the concept of reserve capacity, which reflects the flexibility with respect to changes in terms of demand volume only. The second approach allows for variations in the demand pattern in addition to changes in demand volume in order to more fully capture demand changes. Two models are developed in the second approach to consider two types of capacity flexibility. The total capacity flexibility allows all users to have both route choice and destination choice when estimating capacity flexibility. The limited capacity flexibility estimates how much more demand volume could be added to a fixed demand pattern by allowing the additional demand to deviate from the fixed demand pattern. Numerical examples are provided to demonstrate the different concepts of capacity flexibility for a passenger transportation system under demand changes. [ABSTRACT FROM AUTHOR]

Abstract: The build-operate-transfer (BOT) approach is one of the privatization mechanisms for promoting transportation infrastructure developments by using private funds to construct new infrastructure facilities. In a BOT scheme, it often involves three parties: the government, whose objective is to maximize the benefit defined in terms of social welfare added to the society; the private investors, whose objective is to maximize the profit generated from the investment; and the road users, whose objective is to minimize the inequality of benefit distribution among the road users traveling from different origin–destination pairs. Each of these parties has different objectives that often conflict with each other. In this paper, we develop various optimal road pricing models under demand uncertainty for analyzing the tradeoffs among the three objectives. In addition, a project evaluation framework is developed for assessing the effects of government policy and regulation on the BOT project. Seven cases of the BOT road pricing problem are analyzed: (1) BOT without regulation, (2) BOT with price control regulation, (3) BOT with equity regulation, (4) BOT with construction cost subsidy, (5) BOT with concession period extension, (6) BOT with construction cost subsidy and concession period extension, and (7) BOT with multiple objectives. Numerical results using a real case study of the Ban Pong–Kanchananburi Motorway (BMK) in Thailand are provided to examine the above seven cases. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to introduce a multi-year pavement maintenance programming methodology that can explicitly account for uncertainty in pavement deterioration. This is accomplished with the development of a simulation-based genetic algorithm (GA) approach that is capable of planning the maintenance activities over a multi-year planning period. A stochastic simulation is used to simulate the uncertainty of future pavement conditions based on the calibrated deterioration model while GA is used to handle the combinatorial nature of the network-level pavement maintenance programming. The effects of the uncertainty of pavement deterioration on the maintenance program are investigated using a case study. The results show that programming the maintenance activities using only the expected pavement conditions is likely to underestimate the required maintenance budget and overestimate the performance of pavement network. [Copyright &y& Elsevier]

It is necessary to design and evaluate the effectiveness of walking facilities to accommodate the needs of all pedestrian groups, including individuals with disabilities. The Americans with Disability Act (ADA) standard requires that each facility or part of a facility constructed by, on behalf of, or for the use of a public entity shall be designed and constructed in such manner that the facility or part of the facility is readily usable by individuals with disabilities. The Highway Capacity Manual (HCM) defines walking facility performance using a qualitative measure describing operational conditions, or level of service (LOS). However, how closely pedestrian LOS thresholds correspond to pedestrian groups' perceptions is questionable. To overcome these limitations, a controlled large-scale walking experiment involving individuals with disabilities was conducted at Utah State University (USU). A temporary circuit with the necessary walking facilities was constructed using eight-foot, self-standing walls. In total, 202 (160 without and 42 with disabilities) individuals were recruited to participate in the experiments, with participants asked to pass through the circuit repeatedly. Individuals were tracked using the camera system, and trajectory data extraction was accomplished using a software platform suite. During each experimental session, some participants were randomly selected and asked to complete a questionnaire assessing their walking experience. Using both trajectory and survey data sources, this study explored how a heterogeneous mix of pedestrians perceive and evaluate the operational performance of walking facilities. Specifically, an ordered statistical approach was applied to investigate the effects of environmental density on pedestrians' perceptions. The results indicated that individuals with disabilities were less tolerant of extremely congested environments. Furthermore, the analysis demonstrated that the LOS criteria provided in HCM is inadequate in quantifying the service performance of walking facilities based on the actual perceptions of individuals who participated in the controlled experiment. The findings are expected to improve operational guidelines employed to assess walking facility performance. [ABSTRACT FROM AUTHOR]

This research develops a two-stage approach to estimate a statewide truck origin-destination (O-D) trip table. The proposed approach is supported by two sequential stages: the first estimates the commodity-based truck O-D trip tables primarily derived from the commodity flow database, and the second refines the O-D estimates using the observed truck counts. The first stage uses national commodity flow data from the Freight Analysis Framework Version 3 (FAF 3 ) database to develop a commodity-based truck trip table, while the second stage uses the path flow estimator (PFE) concept to refine the truck trip table obtained from the first stage using the truck counts from the statewide truck count program. The model allows great flexibility for data incorporation at different spatial levels in terms of estimating the statewide truck O-D trip table. To show proof of concept, a case study is conducted using the Utah statewide freight transportation network to demonstrate how the two-stage approach can be implemented in practice. The results show that the proposed approach is applicable for estimating a statewide truck O-D trip table with limited resources, and can be used to conduct truck corridor analysis to determine congested links and potential bottlenecks in Utah. [ABSTRACT FROM AUTHOR]

Travel time reliability has generally been surmised to be an important attribute of transportation systems. In this paper, we study the contribution of travel time reliability in travelers'' route choice decisions. Traveler''s route choice is formulated as a mixed-logit model, with the coefficients in the model representing individual traveler''s preferences or tastes towards travel time, reliability and cost. Unlike the traditional approach involving the use of traveler surveys to estimate model coefficients and thereby uncover the contribution of travel time reliability, we instead apply the methodology to real-time loop detector data, and use genetic algorithm to identify the parameter set that results in the best match between the aggregated results from traveler''s route choice model and the observed time-dependent traffic volume data from loop detectors. Based on freeway loop data from California State Route 91, we find that the estimated median value of travel-time reliability is significantly higher than that of travel-time, and that the estimated median value of degree of risk aversion indicates that travelers value a reduction in travel time variability more highly than a corresponding reduction in the travel time for that journey. Moreover, travelers'' attitudes towards congestion are not homogeneous; substantial heterogeneity exists in travelers'' preference of travel time and reliability. Our results validate results from previous studies involving the California State Route 91 value-pricing project that were based on traditional traveler surveys and demonstrate the applicability of the approach in travelers'' behavioral studies. [Copyright &y& Elsevier]

• Dynamic mode choice in a trip is studied using cumulative prospect theory (CPT). • Penalty cost is fitted as a negative exponential function by satisfaction rating. • The CPT model predicts the within-trip mode shift with a 73.91% accuracy. • Well-educated travelers prefer subways, and car users do not easily change mode. • The mode choice of 77.94% of travelers can be influenced by economic strategies. Travelers' mode choice behavior is jointly influenced by their attributes, such as personal income, vehicle ownership, and travel purpose, and external factors, such as the built environment and traffic state. The mode chosen by a traveler before a trip may change during the trip due to uncertainties associated with transportation systems. A cumulative prospect theory-based dynamic mode choice model that describes the changes in decisions at different travel stages is proposed to investigate the within-trip mode-shifting behavior during daily travel. The journey duration or travel time of a trip is divided into a series of stages, which includes the access or connection time (i.e., the time required for the traveler to reach the station or vehicle), waiting time, in-vehicle time, and parking time. The generalized travel cost includes the expense cost, time cost, and penalty cost. The penalty cost is expressed as a negative exponential function with respect to the punctuality satisfaction rating. A novel method is proposed for calculating the mode choice possibility in terms of the changes in travel cost during a trip, in which the psychological weights at different stages are reflected by the ratio of the actual travel time to the expected travel time. Data from a journey survey involving 637 respondents in Beijing were used to test the model. The results of a random parameter logit model reveal the main influencing factors of each travel mode, and show that economic strategies can influence the mode choice of 77.94% of travelers. The dynamic mode choice model predicted the pre-trip mode and mode shift with accuracies of 90.05% and 67.86%, respectively, both of which are higher than those of the expected utility theory-based model. Moreover, the results show that mode-shifting behaviors during a trip generally occur in public transit mode and/or active travel mode. The study results demonstrate the dynamic characteristic and quantitative change in mode choice behavior in multi-mode daily travel and are beneficial for the study of travel behavior in relation to the traffic environment. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to identify those factors that influence public transportation (PT) competitiveness through an analysis of current passenger loyalty. To accomplish this goal, a structural equation model (SEM) is introduced to construct the theoretical model. A comprehensive set of hypotheses is tested to explore the relationships between service quality, passenger satisfaction, switching costs, attraction of alternatives, and passenger loyalty. This study uses the Shanghai PT in China as the case study to apply the theoretical model. The results reveal that PT service quality is the most important factor that influences a passenger’s willingness to remain loyal. In addition, both switching costs and the attractiveness of cars are significantly related to passenger loyalty. Furthermore, this study proposes some critical suggestions as to how to improve the competitiveness of the Shanghai PT in its ongoing development. Decision makers assisting in the formation of PT policies and strategies will find this information useful. [ABSTRACT FROM AUTHOR]