Ennio Cascetta

Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator

Methods commonly used for estimating origin-destination (O-D) matrices can be divided into three categories: direct sample estimation, model estimation and estimation from traffic counts. In this paper a generalized least squares estimator of the O-D matrix is proposed combining direct or model estimators with traffic counts via an assignment model. The presence of measurement errors and time variability in the observed flows is explicitly considered. A special case is also presented in which the flows are assumed to be deterministically known. For the proposed estimators, means and dispersion matrices are expressed in function of the possible bias in the direct or model estimators and assignment model misspecification. The variance of the O-D matrix obtained with the generalized least squares (GLS) estimators is proved to be lower than that obtained with direct or model estimators but, because of possible biases and misspecifications, it is suggested that their performances have to be compared by using risk or generalized mean square error criteria.

A UNIFIED FRAMEWORK FOR ESTIMATING OR UPDATING ORIGIN/DESTINATION MATRICES FROM TRAFFIC COUNTS

This paper presents an in-depth study of the methodology for estimating or updating origin-to-destination trip matrices from traffic counts. Following an analysis of the statistical foundation of the estimation and updating problems, various basic approaches are reviewed using a generic traffic assignment map. Computational issues related to specific assignment maps and estimation models for both road and transit networks are then discussed. Finally, additional insight into the relative performance of several estimators is provided by a set of test problems with varying input data.

DYNAMIC PROCESSES AND EQUILIBRIUM IN TRANSPORTATION NETWORKS: TOWARDS A UNIFYING THEORY

Traditionally, traffic assignment models, both for within-day static and dynamic demand, have been formulated following an equilibrium approach in which a state ensuring internal consistency between demand (flows) and costs is sought. However, equilibrium analysis is significant under some assumptions on its “representativeness” (coincidence or closeness with the actual attractor of the system) and analytical properties, such as existence, uniqueness, and stability. Moreover, transients due to modifications of demand and/or supply cannot be simulated through equilibrium models, nor can a statistical description of the state of the system, i.e. means, modes, moments and, more generally, frequency distributions of flows over time be obtained. In this paper, interperiodic (day-to-day) dynamic modeling of transportation networks is addressed following two different approaches, namely deterministic and stochastic processes. In both cases several theoretical results are shown by making use of a formal framework covering most models discussed in the literature as well as some possible extensions. Most of the results reported can be extended to cover within-day dynamic models but these models are not explicitly dealt with. Within the framework of deterministic processes the relevance of day-to-day dynamic models for demand/supply interaction in comparison with the traditional user equilibrium approach is discussed, and conditions for coincidence of fixed-point attractors and equilibrium states are stated. Conditions for existence and uniqueness of fixed-point attractors are proposed, generalizing and extending those presented in the literature for user equilibrium. Conditions for stability of both fixed-points and equilibrium states were formulated by making use of results from non-linear dynamic system theory. Moreover, it is possible to devise a new family of “dynamic” algorithms which simulate the system convergence to a fixed-point in order to obtain an equivalent equilibrium state, as opposed to conventional “optimisation” algorithms. In this case the fixed-point stability analysis can be viewed as a convergence analysis for the algorithms specified this way. Conditions for stochastic process regularity are proposed ensuring, among other things, existence and uniqueness of a stationary probability distribution of system states. These conditions generalize and extend results presented in the literature to a wider class of possible dynamic models. Relationships between a deterministic process, together with corresponding fixed-points or equilibrium states, and stochastic probability distribution are also briefly addressed. Finally, some numerical examples confirming theoretical results are reported for a small test network.

RANDOM UTILITY MODELS WITH IMPLICIT AVAILABILITY/PERCEPTION OF CHOICE ALTERNATIVES FOR THE SIMULATION OF TRAVEL DEMAND

Random utility models are undoubtedly the most used models for the simulation of transport demand. These models simulate the choice of a decision-maker among a set of feasible alternatives and their operational use requires that the analyst is able to correctly specify this choice-set for each individual. Some early applications basically ignored this problem by assuming that all decision-makers chose from the same pre-specified choice-set. This assumption may be unrealistic in many practical cases and cause significant misspecification problems (P. Stopher, Transportation Journal of ASCE 106 (1980) 427; H. Williams, J. Ortuzar, Transportation Research B 16 (1982) 167). The problem of choice-set simulation has been dealt within the literature following two basically different approaches: • simulating the perception/availability of an alternative implicitly in the choice model, • simulating the choice-set generation explicitly in a separate model. The implicit approach is more convenient from an operational point of view, while the explicit one is more appealing from a theoretical point of view. In this paper, a different approach to the modeling of availability/perception of alternatives in the context of random utility model is proposed. This approach is based on the concept of intermediate degrees of availability/perception of each alternative simulated through a model (or “inclusion function”) which in turn is introduced in the systematic utility of standard random utility models. This model, named implicit availability/perception (IAP), may be differently specified depending on assumptions made on the joint distribution of random residuals and the way in which the average degree of availability/perception is modeled. In this paper, a possible specification of the IAP model, based on the assumption of random residual distributed as i.i. Gumbel and with the average degree of availability/perception modeled as a binomial logit, is proposed. The paper also proposes ML estimation models in two cases: in the first, only information on alternatives choices is available, while in the second, this information is complemented with others on variables related to a latent (i.e., non-observable) alternatives availability/perception degree (e.g., information on car availability of decision-maker i used as an indirect measurement of the unknown and non-observable availability/perception degree of alternative car for decision-maker i in a modal split). The proposed specification is tested on mode choice data; the calibration results are compared with those of a similar logit specification with encouraging results in terms of goodness of fit.

A Quality-Based Approach to Public Transportation Planning: Theory and a Case Study

ABSTRACT Methodologies for defining standard quality indicators are important to assess service quality in public transport. The article proposes a method to integrate EU service quality standards in the transport planning process. It also investigates the effects of a set of large-scale transport policies in Campania region, Italy, in terms of service quality and ridership variations. Results of 10 years-long transport policies (€3,115 million invested) shows an impressive increase in both service quality and ridership. The article also compares perceived and measured service quality indicators for a new high quality metro line. Results point to new perspectives in modelling users’ travel behavior and related quality measures.

STOCHASTIC USER EQUILIBRIUM ASSIGNMENT WITH EXPLICIT PATH ENUMERATION: COMPARISON OF MODELS AND ALGORITHMS

Abstract In this paper a preliminary analysis of alternative models for “feasible” path generation and choice is presented. In particular a k-shortest path multi-criteria model for path enumeration is explored and different choice models (Logit, recently proposed C-Logit and Probit) are tested by comparing SUE assignment link flows with counts on an urban road network. Flows are also compared for more traditional DUE and SUE Probit implicit path enumeration models. The results obtained show that a limited number (4-7) of paths generated with rather “simple” criteria give satisfactory results, SUE with explicit path enumeration is largely comparable with, and in some cases superior to, traditional implicit SUE and DUE models. Explicit path enumeration allow also the specification of more sophisticated non additive attributes in the utility function of route choice models. From the computational point of view the explicit path C-Logit and Probit SUE algorithms are from three to twenty times superior to the implicit Probit SUE assignment.

Modelling dynamics in transportation networks: State of the art and future developments

Abstract We present a general framework for the simulation of dynamics in transportation networks. Models and algorithms for both within-day and day-to-day dynamic traffic assignment are discussed. The proposed framework includes static models as a particular case, and is general enough to cover most of the existing models.

A joint mode-transit service choice model incorporating the effect of regional transport service timetables

Transit systems offer substantially different services in urban and inter-urban areas; this usually leads to different assumptions about user behaviour in path choice modelling. These differences have not yet been recognised in mode choice modelling. Consistent with the above, in this paper we propose a joint mode-transit service choice model where the elementary alternatives are specific services of the available transit modes together with the private car. Among the attributes of transit services we propose inclusion of the difference between the desired arrival time of the traveller and the arrival time of the specific service (early/late arrival penalty), instead of service/line frequency. Different utility specifications were tested and their results compared for different correlation structures (both nested and cross-nested). In general a high significance was observed in the case of the early/late arrival penalty parameters, indicating the importance of such factors in mode choice.

AN ON-LINE DYNAMIC TRAFFIC PREDICTION MODEL FOR AN INTER-URBAN MOTORWAY NETWORK

This paper specifies the overall structure of an on-line Dynamic Traffic Model System for an inter-urban motorway network. The model predicts traffic conditions on a motorway network in real time, and can be used to provide information on (expected) congestion to a motorway traffic control centre. The model receives traffic data such as traffic flow, average speed and average detector occupancy in real-time from a motorway monitoring system. Additionally, a database of historic traffic information will be used.

An elastic demand schedule-based multimodal assignment model for the simulation of high speed rail (HSR) systems

High speed rail (HSR) represents the future of medium-haul intercity transport. In fact, a number of HSR projects are being developed all over the world despite the financial and economic crisis. Such large investments require reliable demand forecasting models to develop solid business plans aiming at optimizing the fares structures and the timetables (operational level) and, on the other hand, at exploring opportunities for new businesses in the long period (strategic level). In this paper, we present a models system developed to forecast the national passenger demand for different macroeconomic, transport supply, and HSR market scenarios. The core of the model is based on the simulation of the competition between transportation modes (i.e. air, auto, rail), railways services (intercity vs. high speed rail) and HSR operators, using an explicit representation of the timetables of all competing modes/services/runs (schedule-based assignment). This requires, in turn, a diachronic network representation of the transport supply for scheduled services and a nested logit model of mode, service, operator, and run choice. To authors’ knowledge this represents the first case of elastic demand, schedule-based assignment model at national scale to forecast HSR demand. The overall modeling framework has been calibrated based on extensive traffic counts and mixed RP–SP interviews gathered between 2009 and 2011, on the Italian multimodal transportation system. The results of the models estimation are presented, and, some applications to test HSR service options (i.e. fares and timetable) of a new operator entering the HSR market and competing with the national incumbent are discussed.