Showing papers in "Transportation Research Part B-methodological in 2008"

Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings

Abstract: A field experiment in Yokohama (Japan) reveals that a macroscopic fundamental diagram (MFD) linking space-mean flow, density and speed exists on a large urban area. The experiment used a combination of fixed detectors and floating vehicle probes as sensors. It was observed that when the somewhat chaotic scatter-plots of speed vs. density from individual fixed detectors were aggregated the scatter nearly disappeared and points grouped neatly along a smoothly declining curve. This evidence suggests, but does not prove, that an MFD exists for the complete network because the fixed detectors only measure conditions in their proximity, which may not represent the whole network. Therefore, the analysis was enriched with data from GPS-equipped taxis, which covered the entire network. The new data were filtered to ensure that only full-taxi trips (i.e., representative of automobile trips) were retained in the sample. The space-mean speeds and densities at different times-of-day were then estimated for the whole study area using relevant parts of the detector and taxi data sets. These estimates were still found to lie close to a smoothly declining curve with deviations smaller than those of individual links – and entirely explained by experimental error. The analysis also revealed a fixed relation between the space-mean flows on the whole network, which are easy to estimate given the existence of an MFD, and the trip completion rates, which dynamically measure accessibility. 2008 Elsevier Ltd. All rights reserved.

Examining the impacts of residential self-selection on travel behavior: A focus on methodologies

Abstract: Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into seven categories: direct questioning, statistical control, instrumental variables models, sample selection models, joint discrete choice models, structural equations models, and longitudinal designs. This paper reviews and evaluates these alternative approaches with respect to this particular application (a companion paper focuses on the empirical findings of 28 studies using these approaches). We identify some advantages and disadvantages of each approach, and note the difficulties in actually quantifying the absolute and/or relative extent of the true influence of the built environment on travel behavior. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.

An analytical approximation for the macroscopic fundamental diagram of urban traffic

Abstract: This paper shows that a macroscopic fundamental diagram (MFD) relating average flow and average density must exist on any street with blocks of diverse widths and lengths, but no turns, even if all or some of the intersections are controlled by arbitrarily timed traffic signals. The timing patterns are assumed to be fixed in time. Exact analytical expressions in terms of a shortest path recipe are given, both, for the street’s capacity and its MFD. Approximate formulas that require little data are also given. For networks, the paper derives an upper bound for average flow conditional on average density, and then suggests conditions under which the bound should be tight; i.e., under which the bound is an approximate MFD. The MFD’s produced with this method for the central business districts of San Francisco (California) and Yokohama (Japan) are compared with those obtained experimentally in earlier publications.

Designing efficient stated choice experiments in the presence of reference alternatives

Abstract: This paper examines various design strategies that might be employed to construct statistically more efficient stated choice designs in the presence of a reference alternative in a choice set. Using data collected in Sydney in 2004 in the context of trading time and cost attributes associated with alternative tolled and non-tolled routes to drive a car to work, we contrast D-efficient designs (based on a number of ways of pivoting attribute levels around a reference alternative) with the more traditional orthogonal designs and conclude that D-efficiency design strategies produce significantly improved results, in a statistical sense of relative efficiency, than the more traditional orthogonal design. Furthermore, the increased use of computer aided personal survey instruments and internet-based surveys enables researchers to structure the experiments around the very specific experiences of each sampled respondent, adding relevance and comprehendability to the attribute levels being assessed in contrast to other averaging methods to construct reference alternatives.

The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions

Abstract: Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another. A simple and parsimonious multiple discrete-continuous extreme value (MDCEV) econometric approach to handle such multiple discreteness was formulated by Bhat (2005) [Bhat, C.R., 2005. A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B 39(8), 679–707]. within the broader Kuhn–Tucker (KT) multiple discrete-continuous economic consumer demand model of Wales and Woodland (1983) [Wales, T.J., and Woodland, A.D., 1983. Estimation of consumer demand systems with binding non-negativity constraints. Journal of Econometrics 21(3), 263–85]. This paper examines several issues associated with the MDCEV model and other extant KT multiple discrete-continuous models. Specifically, the paper proposes a new utility function form that enables clarity in the role of each parameter in the utility specification, presents identification considerations associated with both the utility functional form as well as the stochastic nature of the utility specification, extends the MDCEV model to the case of price variation across goods and to general error covariance structures, discusses the relationship between earlier KT-based multiple discrete-continuous models, and illustrates the many technical nuances and identification considerations of the multiple discrete-continuous model structure through empirical examples. The paper also highlights the technical problems associated with the stochastic specification used in the KT-based multiple discrete-continuous models formulated in recent Environmental Economics papers.

A Random Regret-Minimization model of travel choice

Abstract: This paper presents an alternative to Random Utility-Maximization models of travel choice. Our Random Regret-Minimization model is rooted in Regret Theory and provides several useful features for travel demand analysis. Firstly, it allows for the possibility that choices between travel alternatives may be driven by the avoidance of negative emotions, rather than the maximization of some form of payoff. Secondly, it acknowledges that traveler decision-making in the context of multiattribute alternatives may not be fully compensatory. Besides this, we show how the Random Regret-Minimization approach is straightforwardly extended towards the case of risky travel choice, using the notion of Expected Regret. Finally, our Random Regret-Minimization model provides a straightforward and intuitive way to incorporate the notion that travelers, when faced with knowledge limitations, may wish to postpone their choice and search for more information first. The developed model is estimated on data from a multimodal travel simulator, where participants could choose between travel alternatives with risky travel times and costs, and the option of travel information acquisition. Estimation results support the validity of the proposed model of Random Regret-Minimization.

Modeling impacts of adverse weather conditions on a road network with uncertainties in demand and supply

Abstract: This paper proposes a novel traffic assignment model considering uncertainties in both demand and supply sides of a road network. These uncertainties are mainly due to adverse weather conditions with different rainfall intensities on the road network. A generalized link travel time function is proposed to capture these effects. The proposed model allows the risk-averse travelers to consider both an average and uncertainty of the random travel time on each path in their path choice decisions, together with the impacts of weather forecasts. Elastic travel demand is considered explicitly in the model responding to random traffic condition in the network. In addition, the model also considers travelers’ perception errors using a logit-based stochastic user equilibrium framework formulated as fixed point problem. A heuristic solution algorithm is proposed for solving the fixed point problem. Numerical examples are presented to illustrate the applications of the proposed model and efficiency of the solution algorithm.

Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model

Abstract: A crucial challenge faced by current microscopic traffic flow models is capturing the relaxation phenomena commonly observed near congested on-ramps: vehicles are willing to accept very short spacings as they enter the freeway, but "relax" to more comfortable values shortly thereafter. This paper introduces a framework to solve this problem using a macroscopic theory of vehicle lane-changing inside microscopic models. In this theory, lane changes take place according to a stochastic process that has been validated in the field, and whose mean value is a function of lane-specific macroscopic quantities. As a consequence, the lane-changing logic becomes very simple compared to existing microscopic lane-changing models, and requires only one extra parameter. The resulting microscopic model is validated with empirical data.

Estimation on stated-preference experiments constructed from revealed-preference choices

Abstract: Constructing stated-preference (sp) experiments from a choice that the respondent made in a revealed-preference setting can enhance the realism of the sp task and the efficacy of preference revelation. However, the practice creates dependence between the sp attributes and unobserved factors, contrary to the independence assumption that is maintained for standard estimation procedures. We describe a general estimation method that accounts for this non-independence and give specific examples based on standard and mixed logit specifications of utility. We show conditions under which standard estimation methods are consistent despite the non-independence. We illustrate the general methodology through an application to shippers’ choice of route and mode along the Columbia/Snake River system.

A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density

Abstract: This paper develops a new method to solve multivariate discrete–continuous problems and applies the model to measure the influence of residential density on households’ vehicle fuel efficiency and usage choices. Traditional discrete–continuous modelling of vehicle holding choice and vehicle usage becomes unwieldy with large numbers of vehicles and vehicle categories. I propose a more flexible method of modelling vehicle holdings in terms of number of vehicles in each category, using a Bayesian multivariate ordinal response system. I also combine the multivariate ordered equations with Tobit equations to jointly estimate vehicle type/usage demand in a reduced form, offering a simpler alternative to the traditional discrete/continuous analysis. Using the 2001 National Household Travel Survey data, I find that increasing residential density reduces households’ truck holdings and utilization in a statistically significant but economically insignificant way. The results are broadly consistent with those from a model derived from random utility maximization. The method developed above can be applied to other discrete–continuous problems.

Predicting traffic flow using Bayesian networks

Abstract: This paper deals with the problem of predicting traffic flows and updating these predictions when information about OD pairs and/or link flows becomes available. To this end, a Bayesian network is built which is able to take into account the random character of the level of total mean flow and the variability of OD pair flows, together with the random violation of the balance equations for OD pairs and link flows due to extra incoming or exiting traffic at links or to measurement errors. Bayesian networks provide the joint density of all unobserved variables and in particular the corresponding conditional and marginal densities, which allow not only joint predictions, but also probability intervals. The influence of congested traffic can also be taken into consideration by combination of the traffic assignment rules (as SUE, for example) with the Bayesian network model proposed. Some examples illustrate the model and show its practical applicability.

Value of time by time of day: A stated-preference study☆

Abstract: This paper proposes an alternative, dynamic framework for estimating time-varying values of travel time savings and values of schedule delay, in which time-preferences are represented as the time-varying excess-willingness-to-pay (EWPT) to being in the one location, over being elsewhere. It is shown how the conventional linear model, with time-independent values of travel time savings and schedule delay costs, is a special case of our model, and that it is implausible particularly in that it implicitly assumes that the willingness to pay for spending a minute at home instead of being in the vehicle does not vary by time of day, even not for very early departures. The framework is applied to SP data representing the respondents' departure time choices for the morning commute. The results suggest that individuals' time-related shadow prices indeed vary strongly over the morning peak, and values of travel time savings are consequently strongly time-dependent, following plausible and intuitive patterns.

Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations

Abstract: This paper deals with the problem of trip matrix and path flow reconstruction and estimation based on plate scanning and link flow observations. To solve the problem, the following steps are used. First, the class F of feasible subsets of scanned links for single users is identified. Second, the conservation laws are stated in terms of flows associated with the class F and path flows. Finally, the path flows are reconstructed based on minimizing a quadratic (weighted) function of the errors with respect to a given set of prior path flows, subject to the conservation law constraints, stated for each of the possible subsets in F , and to the observed information. Once the path flows have been reconstructed, the trip matrix and other link flow estimates become immediately available. In addition, an algorithm for selecting optimal sets of links to be scanned for predicting path flows is provided. Finally, the methods are illustrated by their application to the Nguyen–Dupuis network, showing the important gain obtained, in estimating path and OD-pair flows, if one uses the extra information contained in the scanned data, which is shown to be much more informative than the traditional link count information. This has important practical implications on an efficient estimation of traffic flows.

Stochastic improvement of cyclic railway timetables

Abstract: Real-time railway operations are subject to stochastic disturbances. Thus a timetable should be designed in such a way that it can cope with these disturbances as well as possible. For that purpose, a timetable usually contains time supplements in several process times and buffer times between pairs of consecutive trains. This paper describes a Stochastic Optimization Model that can be used to allocate the time supplements and the buffer times in a given timetable in such a way that the timetable becomes maximally robust against stochastic disturbances. The Stochastic Optimization Model was tested on several instances of NS Reizigers, the main operator of passenger trains in the Netherlands. Moreover, a timetable that was computed by the model was operated in practice in a timetable experiment on the so-called “Zaanlijn”. The results show that the average delays of trains can often be reduced significantly by applying relatively small modifications to a given timetable.

Schedule-based transit assignment model with travel strategies and capacity constraints

Abstract: In this paper, we propose a user equilibrium transit assignment model that takes into account transit schedules and individual vehicle capacities explicitly. The model assumes that passengers use travel strategies that can be adaptive over time and graphically represented as subgraphs. When loading a vehicle, on-board passengers continuing to the next stop have priority and waiting passengers can be loaded on a first-come-first-serve basis or in a random manner. The latter is appropriate when passengers mingle on waiting platforms. When a vehicle is full, passengers unable to board must wait for the next vehicle to arrive. The equilibrium conditions can be stated as a variational inequality involving a vector-valued function of expected strategy costs. Although the function is not necessarily continuous or monotonic, a solution to the variational inequality exists. To find a solution, we propose a method that takes successive averages as its iterates and generates strategies during each iteration by solving a dynamic program. Numerical examples empirically demonstrate that the algorithm converges to an equilibrium solution.

Robust optimal traffic signal timing

Abstract: The performance of signal timings obtained by using conventional approaches for pre-timed control systems is often unstable under fluctuating traffic conditions. This paper presents three models to determine robust optimal signal timings that are less sensitive to fluctuations of traffic flows or perform better against the worst-case scenario without losing much optimality. Computational experiments are conducted to validate the model formulations and solution algorithms.

Integrated daily commuting patterns and optimal road tolls and parking fees in a linear city

Abstract: For decades, the dynamic traffic patterns of morning and evening commutes have been investigated separately, and it is often assumed that they are simple mirror symmetries. In this paper, we use a two-stage differential method to establish a daily traffic pattern that links the morning and evening commutes as an integrated one. Based on a bi-direction bottleneck network with a spatial pattern of parking, we use analytical models to describe travelers’ behavior in choosing departure times in their morning and evening trips, where a commuter’s morning and evening decisions are joined by a parking location. Given fixed parking locations of commuters, we firstly derive the evening commute pattern, which is a Nash equilibrium in the sense that no one can reduce her/his travel cost given other commuters’ decisions. Then the individual evening commute costs are allocated to different parking locations in modeling the morning commuting behavior, and the morning travel pattern is a user equilibrium in the sense that everyone has equal daily travel cost and no one can reduce private travel cost by unilaterally changing travel decisions. Then we propose a time-varying road toll regime to eliminate queuing delay and reduce schedule delay penalty. Furthermore, a time-varying road toll and location-dependent parking fee regime is developed to achieve a system optimum where the morning schedule delay cost is further reduced to the minimum by reversing the spatial order of parking. In view of the fact that road pricing is hard to implement, we propose a location-dependent parking fee regime with no road tolls to optimize the morning commute pattern, without improving the evening commute pattern.

Travel time estimation on a freeway using Discrete Time Markov Chains

Abstract: Travel time is widely recognized as an important performance measure for assessing highway operating conditions. There are two methods for obtaining travel time: direct measurement, or estimation. For the latter, previously developed models tend to underestimate travel times under congested conditions because of the difficulties of calculations of vehicle queue formations and dissipations. The purpose of this study is to develop a model that can estimate travel time on a freeway using Discrete Time Markov Chains (DTMC) where the states correspond to whether or not the link is congested. The expected travel time for a given route can be obtained for time periods during which the demand is relatively constant. Estimates from the model are compared to field-measured travel time. Statistical analyses suggest that the estimated travel times do not differ from the measured travel time at the 99% confidence level.

A quasi-dynamic capacity constrained frequency-based transit assignment model

Abstract: This paper presents a first approach to dynamic frequency-based transit assignment. As such the model aims to close the gap between schedule-based and frequency-based models. Frequency-based approaches have some advantages compared to schedule-based models, however, when modelling highly congested networks a static frequency-based approach is not sufficient as it does not reveal the peaked nature of the capacity problem. The central idea for dealing with the line capacity constraints is the introduction of a "fail-to-board" probability as in some circumstances passengers are not able to board the first service arriving due to overcrowding. The common line problem is taken into account and the search for the shortest hyperpath is influenced by the fail-to-board probability. An assumption that passengers mingle on the platform allows a Markov network loading process which respects the priority of on-board passengers with respect to those wishing to board. The study period is divided into several time intervals and those passengers who failed to board are added to the demand in the subsequent time interval and so might reconsider their route choice. Trips that are longer than one interval are also carried over to subsequent time intervals. The approach is first illustrated with a small example network and then with a case study relating to London, where transit capacity problems are experienced daily during the peak period.

Modeling urban taxi services with multiple user classes and vehicle modes

Abstract: This paper extends the model of urban taxi services in congested networks to the case of multiple user classes, multiple taxi modes, and customer hierarchical modal choice. There are several classes of customers with different values of time and money, and several modes of taxi services with distinct combinations of service area restrictions and fare levels. The multi-class multi-mode formulation allows the modeling of both mileage-based and congestion-based taxi fare charging mechanisms in a unified framework, and can more realistically model most urban taxi services, which are charged on the basis of both time and distance. The introduction of multiple taxi modes can also be used to model the differentiation between luxury taxis and normal taxis by their respective service areas and customer waiting times. We propose a simultaneous mathematical formulation of two equilibrium sub-problems for the model. One sub-problem is a combined network equilibrium model (CNEM) that describes the hierarchical logit mode choice model of occupied taxis and normal traffic, together with the vacant taxi distributions in the network. The other sub-problem is a set of linear and nonlinear equations (SLNE), which ensures the satisfaction of the relation between taxi and customer waiting times, the relation between customer demand and taxi supply for each taxi mode, and taxi service time constraints. The CNEM can be formulated as a variational inequality program that is solvable by means of a block Gauss–Seidel decomposition approach coupled with the method of successive averages. The SLNE can be solved by a Newtonian algorithm with a line search. The CNEM is formulated as a special case of the general travel demand model so that it is possible to incorporate the taxi model into an existing package as an add-on module, in which the algorithm for the CNEM is built in practice. Most of the parameters are observable, given that such a calibrated transport planning model exists. A numerical example is used to demonstrate the effectiveness of the proposed methodology.

A heuristic for the bilevel origin-destination-matrix estimation problem

Abstract: In this paper we consider the estimation of an origin–destination (OD)-matrix, given a target OD-matrix and traffic counts on a subset of the links in the network. We use a general nonlinear bilevel minimization formulation of the problem, where the lower level problem is to assign a given OD-matrix onto the network according to the user equilibrium principle. After reformulating the problem to a single level problem, the objective function includes implicitly given link flow variables, corresponding to the given OD-matrix. We propose a descent heuristic to solve the problem, which is an adaptation of the well-known projected gradient method. In order to compute a search direction we have to approximate the Jacobian matrix representing the derivatives of the link flows with respect to a change in the OD-flows, and we propose to do this by solving a set of quadratic programs with linear constraints only. If the objective function is differentiable at the current point, the Jacobian is exact and we obtain a gradient. Numerical experiments are presented which indicate that the solution approach can be applied in practice to medium to large size networks.

Airport noise regulation, airline service quality, and social welfare

Abstract: This paper explores the impact of airport noise regulation on airline service quality and airfares. It also characterizes the socially optimal stringency of noise limits, taking both noise damage and the various costs borne by airlines and their passengers into account. The analysis also investigates the effect of noise taxes, as well as the optimal level of such taxes. Along with the companion paper by [Girvin, R., 2006a. Airport noise regulation, airline service quality, and social welfare: The monopoly case. Unpublished paper, Program in Transportation Science, UC, Irvine], this work represents the first complete theoretical investigation into the economics of airport noise regulation using a model where the interests of the key relevant stakeholders are captured.

Scenario-based air traffic flow management: From theory to practice

Abstract: Recent developments in solving the single airport ground holding problem use static or dynamic optimization to manage uncertainty about how airport capacities will evolve. Both static and dynamic models involve the use of scenarios that depict different possible capacity evolutions. Dynamic models also require scenario trees featuring branch points where previously similar capacity profiles become distinct. In this paper, we present methodologies for generating and using scenario trees from empirical data and examine the performance of scenario-based models in a real-world setting. We find that most US airports have capacity profiles that can be classified into a small number of nominal scenarios, and for a number of airports these scenarios can be naturally combined into scenario trees. The costs incurred from applying scenario-based optimization, either static or dynamic, to these airports is considerably higher than the “theoretical” optimization results suggest because actual capacities vary around the nominal values assumed in the optimization, and because of uncertainty in navigating scenario trees that the idealized models ignore. Methods for tuning capacity scenarios and scenario trees to mitigate these problems are explored.

Multi-class kinematic wave theory of traffic flow

Abstract: The kinematic wave theory of traffic flow was independently developed by Lighthill and Whitham [Lighthill, M.J., Whitham, G.B., 1955. On kinematic waves. II. A theory of traffic flow on long crowded roads. Procedings of Royal Society A 229, 281–345] and Richards [Richards, P.I., 1956. Shockwaves on the highway. Operations Research 4, 42–51]. The original LWR model was extended in different directions to incorporate more and realistic details. The distinction of classes in traffic flow has received considerable attention recently. This paper proposes a framework for the different existing multi-class extensions of the kinematic wave theory. It turns out that the difference between all models lies in the assumption on how several classes interact. A new model is proposed where classes interact on a non-cooperative way. Slow vehicles act as moving bottlenecks for the fast vehicles, while fast vehicles maximize their speed without influencing slower vehicles. This leads to anisotropic behaviour of the traffic stream. This means that vehicles only react on stimuli in front of them. The new multi-class model is presented and illustrated in the moving bottleneck example of Newell [Newell, G.F., 1998. A moving bottleneck. Transportation Research Part B 32(8), 531–537].

Closed form expressions for choice probabilities in the Weibull case

Abstract: For a probabilistic discrete choice model with independent reversed Gumbel distributed random costs, closed form expressions for the choice probabilities are known under the assumption that the variance is the same for all choice alternatives. This assumption is highly disputable in many cases in reality. In this paper, closed form expressions for the choice probabilities are derived for the case of independent Weibull distributed random costs. It is then necessary to assume that the variance of the Weibull distributed cost is a specific increasing function of its mean, which, however, may be a more natural assumption in many cases. The theoretical results are explained and exemplified in terms of the familiar stochastic user equilibrium (SUE) traffic assignment model.

Estimating the value of leisure from a time allocation model

Abstract: A new approach to calculate the value of leisure is developed and applied. This is derived from a consumer behaviour model that includes goods and activities. A system of time assignment equations is explicitly obtained from which the values of both leisure and work can be analytically calculated using econometrically estimated parameters. This framework is applied using detailed data from three samples in diverse settings: Santiago (Chile), Karlsruhe (Germany), and Thurgau (Switzerland). The empirically estimated values of leisure differ from the wage rate and a theoretical justification is provided.

Efficient scheduling of railway traffic based on global information of train

Abstract: This paper presents an improved simulation method for solving the train scheduling problem to reduce the total travel time on the single-track railway. An algorithm based on the global information of the train is designed to obtain a more effective travel advance strategy of the train than the TAS method proposed by Dorfman and Medanic [Dorfman, M.J., Medanic, J., 2004. Scheduling trains on a railway network using a discrete-event model of railway traffic. Transportation Research Part B 38 (1), 81–98]. Based on the non-local information, we present a train control method with more loose restrains to avoid the deadlock. Moreover, operation conditions of trains under the local information are analyzed in detail, and the improved method can describe the behaviors of acceleration and deceleration of trains. The simulation results show that the total delay time of trains decreases remarkably. This fact means that the improved method can obtain a better schedule than the method proposed by Dorfman and Medanic (2004). Compared with the mathematical programming method, our method has a better computational performance, and can quickly obtain a good feasible meet–pass plan. The approach described in this paper is designed to be a decision support tool for train dispatchers to schedule the trains in real-time.

Statistical inference for time varying origin–destination matrices

Abstract: We consider the problem of estimating a sequence of origin–destination matrices from link count data collected on a daily basis. We recommend a parsimonious parameterization for the time varying matrices so as to permit application of standard statistical estimation theory. A number of examples of suitably parameterized matrices are provided. We propose a multivariate normal model for the link counts, based on an underlying overdispersed Poisson process. While likelihood based inference is feasible given information from sufficiently many network links, we focus on Bayesian methods of estimation because of their ability to incorporate prior information in a natural manner. We derive the Bayesian posterior distribution, but note that its normalizing constant is not available in closed form. A Markov chain Monte Carlo algorithm for generating posterior samples is therefore developed. From this we can obtain point estimates, and corresponding measures of precision, for parameters of the origin–destination matrix. The methodology is illustrated by an example involving OD matrix estimation for a section of the road network in the English city of Leicester.

Effects of high occupancy vehicle lanes on freeway congestion

Abstract: Previous research on the effect of HOV (high occupancy vehicle) lanes on bottleneck flows is extended here to entire freeways using both theory and empirical evidence. The paper shows that if the flows of both high- and low-occupancy vehicles remain invariant before and after a freeway lane is converted to HOV use, then the freeway’s overall traffic density upstream of its bottlenecks is reduced – albeit less than expected – if the HOV lane is underutilized. As a result, HOV lanes can extend queues over longer distances. These expansions can be problematic if the queues’ expanded portions impede traffic on heavily traveled routes that do not pass through the bottleneck. To quantify this effect, the paper analyzes HOV lanes on long, multi-ramp freeways. Formulae are given for the changes in people-hours and vehicle-hours of travel induced by an HOV lane, both when there is uncongested freeway space upstream of the queue to accommodate its expansion, and when there is not. All the inputs to these formulae are either observable or easy to estimate. Hence, the recipes can help evaluate any freeway’s existing, or planned, HOV lane installation. The HOV lanes at all the sites we have analyzed, which are quite typical, add less than 2% to vehicular delay and reduce people delay by more than 10%. These estimates assume no increase in car-pooling. More generally, the paper also suggests how to deploy HOV lanes on city-wide freeway systems and recommends steps to better plan city-wide systems of bus lanes.

GEV-based destination choice models that account for unobserved similarities among alternatives

Abstract: This paper investigates the destination choice problem in transportation planning processes. Most models assume a Multinomial Logit (MNL) form for the problem. The MNL cannot account for unobserved similarities which exist among choice alternatives. The purpose of this paper is to investigate alternative destination choice model structures, focusing on closed-form models. The paper reviews recent GEV formulations and discusses the adaptation of these models to destination choice situation. In addition the paper presents a new model structure composed of three hierarchical levels: it assumes a choice process composed of a broad selection of zones based on a specific land use characteristic (in this case, presence of shopping center) and then a finer selection of zones based on a geographical characteristic (in this case, adjacent zones). To illustrate the similarity measures of selected GEV formulations and the new model structure the paper specifies, estimates and compares destination choice models for weekday shopping trips based on a revealed preference survey. The paper discusses the structure of the proposed choice models, similarity measures and implementation issues related to the GEV destination choice models.