John Polak

THE VALUATION OF RELIABILITY FOR PERSONAL TRAVEL

The paper reviews progress made towards a general theory of the travellers valuation of travel time reliability, and give some indication of recent empirical research in this area. In the progress it brings together a large number of theoretical and empirical results, many of which are only partly in the public domain. Key theoretical results relating to the highway mode are discussed, and expanded to take in the additional complexity of scheduled public transport services. The paper also deals with the problems of collecting empirical data, and describes a recent study carried out by the authors in the context of rail travel, showing how valuations can be derived.

TRAVEL TIME VARIABILITY: A REVIEW OF THEORETICAL AND EMPIRICAL ISSUES.

Over the past several years a number of research projects have attempted to empirically measure behavioural responses to changes in travel time variability. These have generally been built on theoretical models of scheduling choice that account for changes in departure time in response to the expected costs associated with variability. This paper reviews both the theory and empirical results of several projects that estimated coefficients on various measures of variability using stated preference techniques. Gaps in the understanding of these issues are identified and discussed.

On cost-efficiency of the global container shipping network

This paper presents a simple formulation in the form of a pipe network for modelling the global container-shipping network. The cost-efficiency and movement-patterns of the current container-shipping network have been investigated using heuristic methods. The model is able to reproduce the overall incomes, costs, and container movement patterns for the industry as well as for the individual shipping lines and ports. It was found that the cost of repositioning empties is 27% of the total world fleet running cost and that overcapacity continues to be a problem. The model is computationally efficient. Implemented in the Java language, it takes one minute to run a full-scale network on a Pentium IV computer.

Spatial context and the complexity of daily travel patterns : an international comparison

The analysis of travel patterns is an important research topic in transportation research and urban planning. It provides the background information necessary to better understand the complex relationship between urban structure, the transportation system and household travel patterns. To what extent do travel behaviour reflect the properties of the urban structure and the transportation network, or do these patterns largely follow their own regularities? Can different patterns be observed across different space-time settings, or can common patterns be observed, largely independent from such contexts? To better understand these relationships, this paper reports on some of the findings of analyses, conducted to identify underlying structures in various aspects of travel patterns. Travel patterns, derived from activity and travel diary data collected in Portland (USA), Midlands (UK), Fukuoka (Japan), Canadian metropolitan areas, and the South-Rotterdam region (The Netherlands) are compared. The results indicate that travel patterns are largely independent from spatial setting, except for some extreme cases.

Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method

The need to measure urban link travel time (ULTT) is becoming increasingly important for network management and traveler infor mation provision. This paper proposes the use of the k nearest neigh bors (k-NN) technique to estimate ULTT with the use of single loop inductive loop detector (ILD) data. Real-world data from London is used. This paper explores the sensitivity of travel time estimates to var ious k-NN design parameters. It finds that the k-NN method is not particularly sensitive to the distance metric, although care must be taken in selecting the right combination of local estimation method (LEM) and value of k. A robust LEM should be used. The optimized k-NN model is found to provide more accurate estimates than other ULTT methods. To obtain a more accurate estimate of ULTT, a potential application of this approach could be to aggregate GPS probe vehicle ULTT records from different times but the same underlying travel time distribution.

Accounting for Random Taste Heterogeneity in Airport Choice Modeling

The findings from a disaggregate analysis of the choice of airport, airline, and access mode for business travelers living in the San Francisco Bay Area, California, are presented. Aside from formulation of the multidimensional choice process, the main objective is to explore random taste heterogeneity among decision makers in their evaluation of the attractiveness of the different alternatives. The results indicate that this heterogeneity is present in tastes relating to in-vehicle access time, access cost, and flight frequency. Accounting for this heterogeneity leads to gains in model fit but, more important, leads to important insights into the differences in behavior across decision makers and avoids the bias introduced into trade-offs when fixed coefficients are used in the presence of significant levels of heterogeneity. In terms of substantive results, the models also reveal a significant impact of changes in out-of-vehicle access time, indicate a preference for service on jet over turboprop flights, and show that experience plays an important role in air travel choice behavior.

Stochastic model and connectivity dynamics for VANETs in signalized road systems

The space and time dynamics of moving vehicles regulated by traffic signals governs the node connectivity and communication capability of vehicular ad hoc networks (VANETs) in urban environments. However, none of the previous studies on node connectivity has considered such dynamics with the presence of traffic lights and vehicle interactions. In fact, most of them assume that vehicles are distributed homogeneously throughout the geographic area, which is unrealistic. We introduce in this paper a stochastic traffic model for VANETs in signalized urban road systems. The proposed model is a composite of the fluid model and stochastic model. The former characterizes the general flow and evolution of the traffic stream so that the average density of vehicles is readily computable, while the latter takes into account the random behavior of individual vehicles. As the key contribution of this paper, we attempt to approximate vehicle interactions and capture platoon formations and dissipations at traffic signals through a density-dependent velocity profile. The stochastic traffic model with approximation of vehicle interactions is evaluated with extensive simulations, and the distributional result of the model is validated against real-world empirical data in London. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the knowledge of the mean vehicular density dynamics and its probability distribution from the stochastic traffic model, we determine the degree of connectivity in the communication network and illustrate that system engineering and planning for optimizing both the transport (in terms of congestion) and communication networks (in terms of connectivity) can be carried out with the proposed model.

Utility of Schedules: Theoretical Model of Departure-Time Choice and Activity-Time Allocation with Application to Individual Activity Schedules

The timing and duration of activities are important components of individual travel and activity behavior, affecting spatial interaction and mode choice decisions, highway and transit congestion, accessibility, and the general degree of social exclusion or inclusion experienced by the individual. The choices of timing and duration of activities are closely interrelated; however, few researchers have attempted to study this relationship. Instead strong separability assumptions have been made concerning the timing and duration dimensions. A utility theoretical model of joint activity-timing and duration choice is presented that addresses a number of the limitations of existing scheduling and time allocation models. How the theoretical model can be used to provide insight into the nature of the activity-scheduling process is outlined, and an algorithm is presented that can be used to operationalize the time allocation and activity-scheduling model. An estimable empirical form of the theoretical model is subsequently developed in which it is acknowledged that past and present activity and travel behavior decisions affect both existing and expected behavioral outcomes. How the model can be used to develop a number of disaggregate, activity-based accessibility measures is outlined, and with the aid of a simple hypothetical case study, a number of behavioral characteristics of the time-allocation and activity-scheduling model are demonstrated.

New Approach to Modeling Mixed Traffic Containing Motorcycles in Urban Areas

Motorcycles constitute a significant proportion of traffic in many countries but are poorly represented in existing traffic flow theories and simulation software. A new approach to modeling mixed traffic is introduced focusing on depicting the movements of motorcycles. In this study, the characteristic patterns of motorcycle behavior were identified, and the key elements contributing to these patterns were extracted. Then three mathematical models were developed to depict these key elements, which were calibrated by using field data collected at Victoria Embankment in central London. After the calibration procedures, these models were integrated into an agent-based simulation model system. The ability of the simulator to reproduce plausible patterns of car and motorcycle behavior was verified. A number of potential applications of this simulator for the management of mixed traffic streams in urban areas are discussed.

A computationally efficient two-stage method for short-term traffic prediction on urban roads

Abstract Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads.