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Cycling is gaining popularity both as a mode of travel in urban communities and as an alternative mode to private motorized vehicles due to its wide range of benefits (health, environmental, and economical). However, this change in modal share is not reflected in current transportation planning and travel demand forecasting modeling processes. The existing practices to model bicycle trips in a network are not sophisticated enough to describe the full cyclist experience in route decision-making. This is evident in the existing practices' methodology: the all-or-nothing assignment uses single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this article is to develop a multi-class and multi-criteria bicycle traffic assignment model that not only accounts for multiple user classes by acknowledging that there are different types of cyclists with varying levels of biking experience, but also for relevant factors that may affect each user classes behavior in route choice decisions. The multi-class, multi-criteria bicycle traffic assignment model is developed in a two-stage process. The first stage examines key criteria to generate the set of non-dominated (or efficient) routes for each user class, and the second stage determines the flow allocation to efficient routes by user class. Numerical experiments are then conducted to demonstrate the two-stage approach for the multi-class, multi-criteria bicycle traffic assignment model. [ABSTRACT FROM AUTHOR]

This study provides a gradient projection (GP) algorithm to solve the combined modal split and traffic assignment (CMSTA) problem. The nested logit (NL) model is used to consider the mode correlation under the user equilibrium (UE) route choice condition. Specifically, a two-phase GP algorithm is developed to handle the hierarchical structure of the NL model in the CMSTA problem. The Seoul transportation network in Korea is adopted to demonstrate an applicability in a large-scale multimodal transportation network. The results show that the proposed GP solution algorithm outperforms the method of the successive averages (MSA) algorithm and the classical Evan's algorithm. [ABSTRACT FROM AUTHOR]

• Developed a closed-form paired combinatorial weibit route choice model. • Develop MP formulations for PCW-SUE-FD and PCW-SUE-ED models. • Developed a unified algorithm framework for PCW-SUE-FD and PCW-SUE-ED models. • Demonstrate the features of PCW-SUE-FD and PCW-SUE-ED models. • Show the applicability of the solution algorithm to real-world networks. In the traffic assignment problem for predicting traffic flow patterns in a transportation network, it is important to account for route overlap and non-identical perception variance in route choice analysis. In this study, we establish a novel route choice model, named the paired combinatorial weibit (PCW) model, to capture the route overlap and route-specific perception variance. The PCW model retains a closed-form probability solution, which allows the development of an equivalent mathematical programming (MP) formulation for the PCW-based stochastic user equilibrium (PCW-SUE) model. Specifically, we propose two equivalent MP formulations for modeling the fixed demand (FD) and elastic demand (ED), named PCW-SUE-FD and PCW-SUE-ED, respectively. The PCW-SUE-ED model can address the abovementioned two issues in route choice for the FD scheme, but also can consider the effect level-of-service (LOS) in travel choice for the ED scheme. The equivalency and uniqueness of the PCW-SUE-FD and PCW-SUE-ED models are rigorously proved. In addition, a path-based partial linearization algorithm combined with a self-regulated averaging line search strategy is developed to solve the two SUE models. Numerical results are presented to illustrate the features of the PCW-SUE-FD and PCW-SUE-ED models and applicability of the solution algorithm to a real transportation network. [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]

Redundancy is vital for transportation networks to provide utility to users during disastrous events. In this paper, we develop two network-based measures for systematically characterizing the redundancy of transportation networks: travel alternative diversity and network spare capacity. Specifically, the travel alternative diversity dimension is to evaluate the existence of multiple modes and effective routes available for travelers or the number of effective connections between a specific origin-destination pair. The network spare capacity dimension is to quantify the network-wide residual capacity with an explicit consideration of travelers’ mode and route choice behaviors as well as congestion effect. They can address two fundamental questions in the pre-disaster transportation system evaluation and planning, i.e., " how many effective redundant alternatives are there for travelers in the normal or disruptive event ?" and " how much redundant capacity does the network have ?" To implement the two measures in practice, computational methods are provided to evaluate the network redundancy. Numerical examples are also presented to demonstrate the features of the two redundancy measures as well as the applicability of the computational methods. The analysis results reveal that the two measures have different characterizations on network redundancy from different perspectives, and they can complement each other by providing meaningful information to both travelers and planners. [ABSTRACT FROM AUTHOR]

A bstract Reduction of vehicle emissions is a major component of sustainable transportation development. The promotion of green transport modes is a worthwhile and sustainable approach to change transport mode shares and to contribute to healthier travel choices. In this paper, we provide an alternate weibit-based model for the combined modal split and traffic assignment (CMSTA) problem that explicitly considers both similarities and heterogeneous perception variances under congestion. Instead of using the widely-adopted Gumbel distribution, both mode and route choice decisions are derived from random utility theory using the Weibull distributed random errors. At the mode choice level, a nested weibit (NW) model is developed to relax the identical perception variance of the logit model. At the route choice level, the recently developed path-size weibit (PSW) is adopted to handle both route overlapping and route-specific perception variance. Further, an equivalent mathematical programming (MP) formulation is developed for this NW-PSW model as a CMSTA problem under congested networks. Some properties of the proposed models are also rigorously proved. Using this alternate weibit-based NW-PSW model, different go-green strategies are quantitatively evaluated to examine (a) the behavioral modeling of travelers’ mode shift between the private motorized mode and go-green modes and (b) travelers’ route choice with consideration of both non-identical perception variance and route overlapping. The results reveal that mode shares and route choices from the NW-PSW model can better reflect the changes in model parameters and in network characteristics than the traditional logit and extended logit models. [ABSTRACT FROM AUTHOR]

Paradox in the transportation literature is about improving an existing link or adding a new link can actually increase network-wide travel costs or travel costs of each traveler. In this paper, we investigate the stochastic assignment paradox using the multinomial weibit (MNW) model, a new route choice model developed by Castillo et al. (2008), and compare it to the counter-intuitive results of the multinomial logit (MNL) model when an inferior travel alternative is marginally improved. Using a simple two-link network, we derive the conditions for paradoxical phenomenon to occur for both route choice models, and graphically compare and contrast the paradoxical regions. The results show the stochastic assignment paradox depends on how the cost difference is being considered in the route choice model (i.e., absolute cost difference in the MNL model and relative cost difference in the MNW model) to some extent. Hence, the stochastic paradox analysis is extended to a hybrid model that considers both MNW and MNL models (i.e., both relative cost difference and absolute cost difference). The paradox area of the hybrid model is shown to be a combination of the paradox areas of the two models. In addition, the stochastic assignment paradox conditions derived for a simple two-link network are generalized to three cases: (a) one O–D pair with multiple links on a route, (b) multiple O–D pairs, and (c) adding a new link. Analytical solutions, graphical illustrations, and numerical results are provided to demonstrate the stochastic paradox under different conditions. Future research directions are also discussed in the paper. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we extend the α-reliable mean-excess traffic equilibrium (METE) model of Chen and Zhou (Transportation Research Part B 44(4), 2010, 493–513) by explicitly modeling the stochastic perception errors within the travelers’ route choice decision processes. In the METE model, each traveler not only considers a travel time budget for ensuring on-time arrival at a confidence level α, but also accounts for the impact of encountering worse travel times in the (1− α) quantile of the distribution tail. Furthermore, due to the imperfect knowledge of the travel time variability particularly in congested networks without advanced traveler information systems, the travelers’ route choice decisions are based on the perceived travel time distribution rather than the actual travel time distribution. In order to compute the perceived mean-excess travel time, an approximation method based on moment analysis is developed. It involves using the conditional moment generation function to derive the perceived link travel time, the Cornish–Fisher Asymptotic Expansion to estimate the perceived travel time budget, and the Acerbi and Tasche Approximation to estimate the perceived mean-excess travel time. The proposed stochastic mean-excess traffic equilibrium (SMETE) model is formulated as a variational inequality (VI) problem, and solved by a route-based solution algorithm with the use of the modified alternating direction method. Numerical examples are also provided to illustrate the application of the proposed SMETE model and solution method. [Copyright &y& Elsevier]

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: In this paper, we propose a new model called the α-reliable mean-excess traffic equilibrium (METE) model that explicitly considers both reliability and unreliability aspects of travel time variability in the route choice decision process. In contrast to the travel time budget (TTB) models that consider only the reliability aspect defined by TTB, this new model hypothesizes that travelers are willing to minimize their mean-excess travel times (METT) defined as the conditional expectation of travel times beyond the TTB. As a route choice criterion, METT can be regarded as a combination of the buffer time measure that ensures the reliability aspect of on-time arrival at a confidence level , and the tardy time measure that represents the unreliability aspect of encountering worst travel times beyond the acceptable travel time allowed by TTB in the distribution tail of 1− . It addresses both questions of “how much time do I need to allow?” and “how bad should I expect from the worse cases?” Therefore, travelers’ route choice behavior can be considered in a more accurate and complete manner in a network equilibrium framework to reflect their risk preferences under an uncertain environment. The METE model is formulated as a variational inequality problem and solved by a route-based traffic assignment algorithm via the self-adaptive alternating direction method. Some qualitative properties of the model are rigorously proved. Illustrative examples are also presented to demonstrate the characteristics of the model as well as its differences compared to the recently proposed travel time budget models. [Copyright &y& Elsevier]

• Develop a new road network design tool for maximizing travelers' route diversity. • Maximize the total number of O-D pairs with alternative distinct routes. • Formulate the problem of the maximum number of distinct paths between an O-D pair. • Reformulate the model as a single-level MILP for global optimality. • Demonstrate the flexibility of using the model to enhance network redundancy. In this paper, we develop a new tool to enhance the resilience of transportation networks by optimizing route diversity redundancy from travelers' perspective. The decision of where to add new links is made to generate as many origin–destination (O-D) pairs with alternative distinct routes as possible to potentially alleviate the impact of disruptions. Mathematically, we formulate the problem as a binary integer bi-level program (BLP), where the upper-level subprogram maximizes the total number of O-D pairs with alternative distinct routes subject to a budgetary constraint, and the lower-level subprogram counts the maximum number of distinct routes between each O-D pair under a given design scheme. Computationally, the BLP model can be equivalently reformulated as a single-level mixed-integer linear program (MILP) by using the equivalent continuous relaxation and optimality conditions of the lower-level subprogram and linearization techniques for the complementarity conditions. The globally optimum solution of the MILP structure of the reformulated model can be obtained using existing algorithms in commercial software packages. Numerical examples are provided to demonstrate the validity, features, and flexibility of the proposed route diversity-oriented network design model in terms of optimizing the network redundancy. The proposed route diversity redundant network design contributes toward enhancing network resilience against disruptions in the pre-disaster stage of transportation network planning and assists in redundancy-oriented time-dependent network growth. It can also serve as an upper-bound solution for the long-term network design problem when travelers' route choice behaviors and travel demands are considered in the design process. [ABSTRACT FROM AUTHOR]

• Devise a truncated path choice model to endogenously distinguish inactive paths • Derive an equivalent convex program formulation of the truncated SUE solution • Require no additional parameter compared with conventional SUE models • Develop a tailored gradient projection algorithm with global convergence This paper develops a convex programming approach with a truncated path choice model to resolve a fundamental drawback of conventional stochastic user equilibrium (SUE) models; that is, assign strictly positive flow to a path, irrespective of the length of its travel time. The centerpiece of the truncated path choice model is to truncate the path choice probability to zero when the travel time exceeds travelers' maximum acceptable travel time , while the choice probabilities of other paths follow the utility maximization principle. Although the truncated path choice model has a non-smooth expression, the truncated SUE condition can be equivalently formulated as a twice-differentiable convex mathematical programming (MP), which has a simple structure comparable to that of Fisk's MP formulation of the multinomial logit SUE model. Moreover, the origin–destination pair-specific parameter of the maximum acceptable travel time, which is explicitly expressed in the truncated path choice model, is endogenized and implicit in the devised MP formulation. This substantially decreases the computational effort required for parameter calibration. The desirable MP formulation enables us to establish the existence and uniqueness of equilibrium path flow under mild assumptions and to develop convergent and efficient solution algorithms. Specifically, we develop a path-based gradient projection algorithm incorporating an Armijo-type Barzilai-Borwein step size scheme for solving the truncated SUE model. Numerical results demonstrate the validity of the truncated SUE model and the efficiency and robustness of the devised algorithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] We illustrate shortcomings of existing travel time reliability UE models. [•] We introduce bi-objective user equilibrium models to overcome the shortcomings. [•] We formulate bi-objective versions of two travel time reliability UE models. [•] We prove that the UE models are special cases of the bi-objective formulations. [•] We propose a general bi-objective formulation as a general modelling framework. [Copyright &y& Elsevier]

Abstract: We consider a transportation network with a set of origins and a set of destinations. Given a set of budgets for the residential and employment developments, and network enhancement, the problem is one of allocating the resources within the system, so that the probability of overloading the links in the network is minimized. For the improved transportation system with new household and employment distribution patterns, a combined distribution and assignment model is used to map the land-use pattern to the link-loading pattern in the network. Assuming that the actual demand of each origin–destination (O–D) pair follows a certain distribution, the road users choose their destination and route in accordance with the user equilibrium principle that is based on the long-run perceived travel cost. Moreover, it is assumed that despite the short-term demand fluctuation, the O–D-link choice proportion remains unchanged for the choices of destination and route, and the stochastic demand of all O–D pairs are independent. Explicit formulae for the mean and variance of the traffic volume on each link are derived. Using the central limit theorem, the probability that the traffic volume does not exceed the link capacity can be estimated, from which we can calculate a network reliability index. This index represents the probability that all links in the network are within the respective capacities. The problem can be formulated as a bi-level program, in which the upper-level sub-program maximizes the network reliability index with respect to the residential and employment allocations and network enhancements, whereas the lower-level subprogram is the combined distribution and assignment model with long-run travel cost functions. The problem is solved by a genetic algorithm. A numerical example is used to demonstrate the effectiveness of the methodology. [ABSTRACT FROM AUTHOR]

• Develop a new day-to-day dynamic network vulnerability analysis approach. • Consider day-to-day network performance fluctuation based on a new day-to-day model. • Use the Weibit model to capture travelers' subjective perception error uncertainty. • Use the mean-excess travel time measure to capture objective travel time uncertainty. • Identify the critical bridges in the Winnipeg network. The disruption of critical components in a transportation network can bring about severe network performance degradation and requires a relatively long period to recover, which would lead to commuters' day-to-day route choice adjustment. Under disruptions, there would be greater travel time variability (objective uncertainty) and travelers' perception error uncertainty (subjective uncertainty) in the transportation network. However, no vulnerability analysis method in the literature can consider the day-to-day network performance fluctuation under uncertainties. In this paper, we develop a new day-to-day dynamic network vulnerability analysis approach that allows the consideration of day-to-day network performance fluctuation based on a new day-to-day dynamic model considering both objective travel time uncertainty and subjective perception error uncertainty. Compared to most existing day-to-day models that either adopt User Equilibrium (UE) or Logit-based route choice criterion, the new day-to-day model has two advantages: (1) the Weibit model is used to capture travelers' subjective perception error uncertainty, which does not have the perfect information assumption in the UE model, or the identically distributed perception error assumption in the Logit model; and (2) the mean-excess travel time (METT) concept is used to capture the objective travel time uncertainty, which handles the inconsideration of travel time variability in most day-to-day models while remaining computational tractability. Based on the proposed day-to-day dynamic model, we develop a new component importance metric for network vulnerability analysis. This new metric characterizes the post-disruption day-dependent consequences to alleviate the limitation of only assessing the final static equilibrium consequence as in the existing studies of vulnerability analysis. Numerical examples are provided to demonstrate the features of the proposed day-to-day dynamic model and the new component importance metric, as well as their applicability in identifying the critical bridges in the Winnipeg network. The proposed approach provides a new decision support tool for planners and managers in assessing the consequences of disruptions, identifying the critical components, and determining the recovery schedules after disruptions. [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]

• Underground pedestrian systems leveraged by metro development. • Cavern-type stations feature a new lift-only entrance design at hilly terrain. • Pedestrian surface-to-surface route choices between underground and surface networks. • Lift-only feature demotes the use of underground space for everyday walking. • Success in promoting underground space use for the elderly and disabled people. The intensive development of the metro system has enabled the building of underground infrastructure that facilitates pedestrian movement from below ground to the surface, and between surface areas. However, it is unclear how willing residents are to use the network of underground spaces of newly built stations as a pedestrian system parallel to the surface street network. This case study investigates an underground pedestrian system connected to cavern-type metro stations in Hong Kong that feature new lift-only entrances in hilly terrain. Based on a face-to-face survey conducted in a new station area of a hilly neighborhood, a binary mixed logit model is developed to estimate the effect of route attributes, trip characteristics, socio-demographics, and walking preferences on the decision to use alternative underground walking routes. Binary choice sets are based on the shortest under- and aboveground paths derived from a three-dimensional pedestrian network, as are alternative-specific variables for distance, estimated walking time, mobility-aid facilities (such as lifts), and walking barriers (such as staircases). The results indicate that route attributes, especially travel time and the existence of a lift-only exit, have an important effect on the intention to use underground routes. The results also show that older adults, the disabled, and those living near stations are more willing to use underground walking routes. These findings can be used by urban/transport planners to support strategies concerned with the future implementation of underground pedestrian networks in three-dimensional multi-layered cities. [ABSTRACT FROM AUTHOR]