A modeling approach for matching ridesharing trips within macroscopic travel demand models

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.     

Goodness-of-fit measures for probabilistic travel demand models

This paper is concerned with the assessment of the goodness-of-fit of nonlinear models of the type currently being used in the development of the disaggregate, behavioral travel demand approach. These models are emerging as a potential new technique for many transportation planning problems, although much research is yet needed before they are sufficiently developed for operational use. In order to pursue the necessary research, and also for the later assessment of operational models, it is necessary to have adequate measures of the goodness-of-fit.The paper examines the adequacy of standard measures of goodness-of-fit as applied to any nonlinear estimating equation and they are found to be inappropriate and inadequate. A little-known statistic, called the correlation ratio, is then defined and derived, and is explored as a substitute for the standard measures. In both theoretical and empirical tests, the correlation ratio is found to be a significantly more useful and appropriate measure of goodness-of-fit.Some further properties of the correlation ratio are examined, and the ratio is found to possess some degree of arbitrariness when applied to typical travel demand models. This arbitrariness, however, only impairs the usefulness of the correlation ratio in the absolute assessment of a model, but not for the comparative assessment of two or more models. Finally, a number of research tasks, relating to the correlation ratio, are identified.     

Defining domains for models of travel demand

Travel demand models implicitly assume that people respond to changes in a continuous way. This is in contrast to the physical sciences, where discontinuous response is a common phenomenon and is embodied in such concepts as sub-critical and supercritical states.Recent studies have shown that responses to transport policies differ in degree and kind according to the nature and severity of the stimulus and the types of people affected. Response patterns may be categorised by the extent to which they involve adjustments to spatio-temporal or inter-personal linkages. This paper identifies four response domains, with a further distinction between permissive and forced changes.Most travel demand models are designed to operate within an independent, forced (and to a less extent independent permissive) domain and their forecasts become unreliable when responses lie outside that domain. Conversely, a model designed for a more complex domain is unnecessarily cumbersome where simpler responses apply. This paper describes the types of model which are appropriate for each domain and discusses how the effects of a policy may be assigned to the correct domain(s).     

Evaluation of transfer methods for spatial travel demand models

The main goal of the research was to compare alternative methods of spatial transfer as a function of sample size. The study was based on the mobility surveys conducted in the Helsinki Metropolitan Area in 1995 and in the Turku region in 1997. The Helsinki Metropolitan Area data base was used to estimate the models that were to be transferred. The data base in the Turku region represents the application context to which the estimated Helsinki Metropolitan Area models are transferred. The transfer procedures examined were Bayesian updating, combined transfer estimation, transfer scaling, and joint context estimation procedures. To explore the impact of sample size on transferring performance, model transferability was tested using six different sample sizes. The model transferability was examined by comparing the transferred models to the models estimated using the entire set of the data which can be regarded as the best estimate representing “the real situation”. The results indicated that joint context estimation gives the best prediction performance in almost all cases. In particular, the method is useful if the difference in the true parameters between the two contexts is large or only some of the model coefficients are precise. The applicability of joint context estimation can be improved by viewing the coefficients as variable-oriented and emphasizing precise and imprecise coefficients differently.     

Geographic information system design for network equilibrium-based travel demand models

Travel demand analyses are useful for transportation planning and policy development in a study area. However, travel demand modeling faces two obstacles. First, standard practice solves the four travel components (trip generation, trip distribution, modal split and network assignment) in a sequential manner. This can result in inconsistencies and non-convergence. Second, the data required are often complex and difficult to manage. Recent advances in formal methods for network equilibrium-based travel demand modeling and computational platforms for spatial data handling can overcome these obstacles. In this paper we report on the development of a prototype geographic information system (GIS) design to support network equilibrium-based travel demand models. The GIS design has several key features, including: (i) realistic representation of the multimodal transportation network, (ii) increased likelihood of database integrity after updates, (iii) effective user interfaces, and (iv) efficient implementation of network equilibrium solution algorithms.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

A demand model for recreational travel

A model is developed to describe and to predict the patterns of regional recreational travel. The model is designed in such a manner to allow its calibration and use without the need to conduct extensive travel surveys in a large region. To allow its use for prediction, the model is based on a causal structure and attempts to derive recreational travel demand from behavioural variables. The main hypothesis of the model is that the amount of recreational travel a recreation area attracts is affected by the accessibility of this area to points of demand potential and by its attractiveness relative to the recreation areas.

The calibration is founded on actual data on recreational travel to national forests in California, U.S.A. It is found in the calibration that accessibility to demand potential is the single most important determinant of recreational travel attraction. A simple relationship is derived to relate travel to each national forest to the relative accessibility of the forest. The model is calibrated and statistically validated.

It is suggested that when constructing travel demand models simplicity be sought, even at the risk of the loss of some explanatory power. In the calibration of such models statistical significant is more important than the ability to reproduce observed patterns.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

     

Investigation and analysis of evidence of asymmetric churn in travel demand models

There is a large amount of research work that has been devoted to the understanding of travel behaviour and for the prediction of travel demand and its management. Different types of data including stated preference and revealed preference, as well as different modelling approaches have been used to predict this. Essential to most travel demand forecasting models are the concepts of utility maximisation and equilibrium, although there have been alternative approaches for modelling travel behaviour. In this paper, the concept of asymmetric churn is discussed. That is travel behaviour should be considered as a two way process which changes over time. For example over time some travellers change their mode of travel from car to bus, but more travellers change their mode from bus to car. These changes are not equal and result in a net change in aggregate travel behaviour. Transport planners often aim at producing this effect in the opposite direction. It is important therefore to recognise the existence of churns in travel behaviour and to attempt to develop appropriate policies to target different groups of travellers with the relevant transport policies in order to improve the transport system. A data set collected from a recent large survey, which was carried out in Edinburgh is investigated to analyse the variations in departure time choice behaviour. The paper reports on the results of the investigation.     

Further evidence on the temporal stability of disaggregate travel demand models

Current evidence on the transferability of disaggregate travel demand models is inconclusive. Adding to this body of research, the present analysis focuses upon the temporal characteristics of work trip behavior in the San Francisco Bay Area. Using before and after data sets associated with the BART Impact Travel Study, multinomial logit models of work trip modal choice are estimated. The results indicate that the general form and the coefficient estimates of a pre BART model are transferable in time. Moreover, when updated to reflect BART's presence, the model's predictive success and its implied elasticity measures are generally accurate, relative to those implied by reestimating the entire model on post BART data. Finally, as economic theory would predict, elasticity measures of the service related variables were found to increase over time.     

Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes

Transportation - The paper presents an investigation of the temporal transferability of activity scheduling process models and a Meta model of activity scheduling processed by using repeated...     

Telecommunications and travel demand and supply: Aggregate structural equation models for the US

Disaggregate studies of the impacts of telecommunications applications (e.g. telecommuting) on travel have generally found a net substitution effect. However, such studies have all been short-term and small-scale, and there is reason to believe that when more indirect and longer-term effects are accounted for, complementarity is the likely outcome. At least two aggregate studies have focused on the relationships between telecommunications and travel from economic perspectives (consumer and industry). However, both use the monetary value of consumption or transactions rather than actual activity measures (e.g. miles, number of calls), and neither fully explains the direct and indirect causal relationships between the two. The purpose of this study is to develop a conceptual model in a comprehensive framework, considering causal relationships among travel, telecommunications, land use, economic activity, and socio-demographics, and to explore the aggregate relationships between telecommunications and travel, using structural equation modeling of national time series data spanning 1950–2000 in the US. In this paper we focus on number of telephone calls as the measure of telecommunications, and passenger vehicle–miles traveled as the measure of transportation. Future research will investigate additional measures of these two constructs. Our empirical results strongly support the hypothesis that telecommunications and travel are complementary. That is, as telecommunications demand increases, travel demand increases, and vice versa. These results offer a more realistic picture to policy makers and transportation planners than has been available till now, and suggest useful directions for them to develop transportation or telecommunications strategies designed to reduce traffic congestion, air pollution, and energy consumption.     

Estimating the demand for urban bus travel

The disadvantages of conventional transportation study models, in particular their large data requirements and their weaknesses in dealing with changes in trip generation rates have led to a need for a simple model that can quickly and at low cost examine alternative public transport strategies.This paper investigates simple economic models of bus demand, examines alternative variables that can be used and discusses some alternative model forms. It demonstrates the results of a model using data from twelve urban bus operators in Britain and compares the results with those from other types of study. The model utilises fare and service quality elasticities to explain the decline in passengers on urban bus services, and derives an average elasticity with respect to fare changes of –0.31 and with respect to service quality changes of +0.62. It is estimated that fare rises accounted for 13% of the 43% decline in passengers over the last fifteen years, vehicle mileage reductions for 14.3% and that only 15.7% was due to such factors as rising car ownership which are often given as the cause of declining bus patronage.The results, by showing that passengers are far more sensitive to changes in service than they are to fare rises, are a useful guide to the broader public transport policy issues, and the paper concludes that the model does provide a useful method of forecasting public transport demand at a strategic level. Further work is needed, however, to establish more accurate forecasts for different types of passenger and studies are now being undertaken to establish these and to construct an operational forecasting model that can be applied with only limited data requirements     

Micro-simulation of daily activity-travel patterns for travel demand forecasting

The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns. This revised version was published online in June 2006 with corrections to the Cover Date.     

Framework for designing sample travel surveys for transport demand modelling in cities

Transportation - Travel surveys in cities remain the main source of information for obtaining people’s trip characteristics and developing transport models that serve to predict the...     

An aggregate demand model for intercity passenger travel in Sri Lanka

Inspite of the inherent weaknesses in aggregate demand models, they continue to be used in everyday applications, especially in developing countries. The largely data intensive disaggregate model preclude its application in many cases. This paper attempts the formulation and calibration of an aggregate total demand model for estimating inter-district passenger travel by public transport in Sri Lanka. In its process, an investigation is made of the common problems in the aggregate approach while examining possible remedial measures to improve the accuracy and (hence) the usability of the aggregate model. It is argued that commonly used variables and functional forms are inappropriate for making accurate estimates in developing countries. Consequently, the model calibration is shown to incorporate variables representing urbanisation, under-development, transfers, a mode-abstract cost function and intrinsic features. The necessity for functional form for each variable to be based on behavioral assumptions that are tested using the Box-Cox transformation for ensuring the best fit of the data is also observed. Although, the model form was calibrated for Sri Lanka, the model is generalised in order for its applications to other countries as well as, both, inter-district and intercity travel demand estimation.     

A randomized controlled trial in travel demand management

Transportation - This paper presents a trial aimed at reducing parking demand at a large urban employer through an informational campaign and monetary incentives. A 6-week randomized controlled...     

A time-use activity-pattern recognition model for activity-based travel demand modeling

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).

     

Nested logit models for mixed-mode travel in urban corridors

The nested logit (NL) model is a generalisation of the well-known multinomial logit (MNL) model which copes with its “independence from irrelevant alternatives” problem, at the expense of more difficult calibration and use. Mixed-mode movements (i.e. park-and-ride) are by nature not independent of competing single-mode options and have, therefore, traditionally been inadequately modelled in most empirical applications. This paper reports on the specification, estimation, testing and comparison of MNL and NL models using disaggregate data of work trips in an urban corridor, where choice was among several alternatives including mixed-mode options. It was found that the more general NL model was more adequate, not only in theory but in practice. The paper concludes by comparing the disaggregate NL model with previously calibrated aggregate NL models for the same corridor using a different data set.