Transport Demand Models Research Articles

Modelling the Effect of Chronic Wasting Disease on Recreational Hunting Site Choice Preferences and Choice Set Formation over Time

Chronic wasting disease (CWD) is a prion disease that affects deer, elk and other cervid wildlife species. Although there is no known link between the consumption of CWD affected meat and human health, hunters are advised to have animals from CWD affected areas tested and are advised against consuming meat from CWD infected animals (Government of Alberta 2010). We model hunter response to the knowledge that deer in a wildlife management unit have been found to have CWD in Alberta, Canada. We examine hunter site choice over two hunting seasons using revealed and stated preference data in models that incorporate preferences, choice set formation, and scale. We compare a fully endogenous choice set model using the independent availability logit model (Swait in Probabilistic choice set formation in transportation demand models. Dissertation, MIT, 1984) with the availability function approach (Cascetta and Papola in Transp Res C 9(4):249–263, 2001) that approximates choice set formation. We find that CWD incidence affects choice set formation and preferences and that ignoring choice set formation would result in biased estimates of impact and welfare measures. This study contributes to the broader recreation demand literature by incorporating choice set formation, scale and temporal impacts into a random utility model of recreation demand.

Transport modelling in the age of big data

ABSTRACTNew Big Data sources such as mobile phone call data records, smart card data and geo-coded social media records allow to observe and understand mobility behaviour on an unprecedented level of detail. Despite the availability of such new Big Data sources, transport demand models used in planning practice still, almost exclusively, are based on conventional data such as travel diary surveys and population census. This literature review brings together recent advances in harnessing Big Data sources to understand travel behaviour and inform travel demand models that allow transport planners to compute what-if scenarios. From trip identification to activity inference, we review and analyse the existing data-mining methods that enable these opportunistically collected mobility traces inform transport demand models. We identify that future research should tap on the potential of probabilistic models and machine learning techniques as commonly used in data science. Those data-mining approaches are designed to handle the uncertainty of sparse and noisy data as it is the case for mobility traces derived from mobile phone data. In addition, they are suitable to integrate different related data sets in a data fusion scheme so as to enrich Big Data with information from travel diaries. In any case, we also acknowledge that sophisticated modelling knowledge has developed in the domain of transport planning and therefore we strongly advise that still, domain expert knowledge should build the fundament when applying data-driven approaches in transport planning. These new challenges call for a multidisciplinary collaboration between transport modellers and data scientists.

The use of state-of-the-art transport models by policymakers – beauty in simplicity?

Transport demand models have a long history of being a major tool in transport policy making. However, whether they are truly used in decision-making processes, and if so, whether the knowledge they provide is actually understood, is questionable. The potential contribution they can make and the importance of such models is not disputed; however, evidence shows that many issues arise with their actual use that severely limits their potential contribution. Based on case study methodology and analysis of the use of models in the transport policy processes in two countries, the UK and Israel, this paper aims to provide empirical evidence of the issues contributing to limiting the potential contribution of models and to make recommendations for better utilisation of the knowledge they can produce. The main conclusion reached is that transport models must be made simpler if they are to contribute more than they currently do to decision-making in transport policy and planning.

Transportation needs of low income population: a policy analysis for the Washington D.C. metropolitan region

Public transportation is part of the economic and social fabric of metropolitan areas, especially to low income individuals, whom are often totally dependent on this service for their daily activities. The role that transit plays on the connection between residential locations and employment opportunities is crucial in creating and implementing programs that will improve and maintain transit and vehicle ownership options viable for vulnerable segments of metropolitan residents. This study proposes the use of the logsum measure obtained from transportation demand models to assess the accessibility of a target population. In this sense, the Maryland State Travel model is used to evaluate the Washington Metropolitan area. This paper analyzes the socio-demographic characteristics of low income individuals, job availability, travel patterns and trip chaining and evaluates policies aiming at improving accessibility by car and public transport within the study area. The results show that policies promoting investment on public transportation would yield higher benefits to the low income population when compared to the ones that promote lowering vehicle operational cost. Interestingly, the results also highlight the fact that extremely low income households receive almost no benefit from these incentives, indicating that reasons other than lack of transportation means might be hindering them to reach job opportunities.

Stated preference surveys in transport demand modeling: disengagement of respondents

This work addresses the behavior and cognitive problems associated with stated preference surveys, more specifically respondents' disengagement. The paper provides an extensive introduction and literature review, and the analysis of the extent of the respondents' disengagement observed in a high-speed railway survey conducted in Portugal. The results show a high level of disengaged respondents, especially for the car users; evidence of learning rather than survey fatigue as a respondent progress through the questions, and connection of disengagement and social-economic characteristics of a person. The findings suggest that disengagement has an important impact on the travel demand modeling results. Finally, the study analyzes possible reasons of disengagement and suggests possible future solutions to the issue. The paper aims at helping to improve the conduct and usage of data in the future.

Gender and Age Differences in the Travel Behavior – A Novi Sad Case Study

This work presents the results obtained by analyzing behavior patterns of transport users in Novi Sad (Republic of Serbia) with respect to their gender and age. Novi Sad is the second largest city in the Republic of Serbia, and is recognized as a regional administrative, commercial, tourist and cultural center. In addition to its favorable geographical, topographical and climatic conditions, its street and public transport network has considerably developed in recent decades, while its innermost districts have a well developed network of bicycle paths. Presently, the city is characterized by extremely high participation of non-motorized trips (about 48%) and approximately equal contribution of public transport and private vehicles to the motorized travel volume. The aforementioned analysis utilized the information sourced from the NOSTRAM database and the Novi Sad Transport Model. In order to form such a comprehensive database of daily activities, a series of traffic surveys was carried out (household surveys, surveys on the outer cordon, public transport passenger counting, parking space utilization monitoring, etc.). Statistical analysis of the household survey data enabled a comparison of the male and female core travel characteristics. The same methodology was subsequently employed in comparing travel characteristics pertinent to different age groups (children, teenagers, students, working-age population represented by several categories, and pensioners). Data analysis revealed gender differences in the utilization of various transport modes, as well as with respect to travel purpose and daily distribution of key activities. It can be said that, due to the traditional societal values and poor economic conditions, women tend to use passenger car less frequently than men do. The results related to different age groups prompted a discussion on transport accessibility to various population strata and employment level as the potential causes of the observed patterns and low mobility among children.The contribution of this paper is multifaceted, as it provides valuable information for further social and traffic studies, as well as for the creation of sustainable mobility plans for Novi Sad. The patterns revealed here can be applied in the development of mobility schemes of other regional cities characterized by similar economic development and cultural-historical heritage. The value of this work is reflected in the creation of a solid basis for the definition of mathematical models of transport demand and identification of transport policy measures directed toward sustainable mobility.

Estimating Incoming Cross-border Trips Through Land Use data Resources – A Case of Karachi City

A multitude of studies have been motivated on the association between land use, urban settings and transport infrastructure to assist policy makers in sustainable planning. Alike, incorporation of cross-border trips have been an integral part of transportation demand models through external surveys. The present study seeks to explore the Incoming Cross Border Traffic (ICBT) into a study area based on the characteristics of a study area that attracts cross-border trips from outside region. This paper presents an analysis of cross-border trips in Karachi Metropolis, largest city of Pakistan, through Household Individual Survey (HIS-2010) and land use data from alternative resources. Results reveal that land use particulars, socioeconomic characteristics and travel attributes of individuals significantly influences cross-border trips and this effect varies spatially. Work, shopping and Education trips are discussed through separate models in this paper with a number of practical insights to policy makers for sustainable development of city. This study contribute in elucidating travel behaviour through land use parameters and also persuade professionals to integrate estimation of cross-border trips by socioeconomic parameters, in transport forecasting models.

Compatibilidade entre funções de atraso e o HCM em rodovias paulistas

Neste trabalho foram identificadas na literatura e descritas as funções de atraso mais comumente usadas nos pacotes de modelagem de demanda por transporte, a saber: (1) BPR; (2) Conical; (3) Função de Akçelik; e (4) Logística. Em seguida, foi testada a compatibilidade entre essas quatro VDFs e os modelos atuais do HCM, calibrados para o ambiente rodoviário brasileiro. Como principais conclusões, tem-se que os melhores ajustes foram obtidos com o uso da função do BPR e logística. Por outro lado, as funções Conical e de Akçelik devem ser empregadas com cautela, pois tendem a subestimar o atraso em condições de fluxos de tráfego baixos a médios, comuns em rodovias rurais. Especificamente com relação às rodovias de pista simples, a função logística se mostrou interessante, devido a capacidade de refletir atrasos mais acentuados para baixos fluxos de tráfego, e produzir maiores atrasos em condições congestionadas, característica desejável em modelagem de demanda.

Happy or liberal? Making sense of behavior in transport policy design

Appropriate microeconomic foundations of mobility are decisive for successful policy design in transportation and, in particular, for the challenge of climate change mitigation. Recent research suggests that behavior in transportation cannot be adequately represented by the standard approach of revealed preferences. Moreover, mobility choices are influenced by factors widely regarded as normatively irrelevant. Here we draw on insights from behavioral economics, psychology and welfare theory to examine how transport users make mobility decisions and when it is desirable to modify them through policy interventions. First, we explore systematically which preferences, heuristics and decision processes are relevant for mobility-specific behavior, such as mode choice. We highlight the influence of infrastructure on the formation of travel preferences. Second, we argue that the behavioral account of decision-making requires policy-makers to take a position on whether transport policies should be justified by appealing to preference satisfaction or to raising subjective well-being. This distinction matters because of the (i) influence of infrastructure on preference formation, (ii) health benefits from non-motorized mobility, (iii) negative impact of commuting on happiness and (iv) status-seeking behavior of individuals. The orthodox approach of only internalizing externalities is insufficient because it does not allow for the evaluation of these effects. Instead, our analysis suggests that transport demand modeling should consider behavioral effects explicitly.

Consideration of transport logistics hubs in freight transport demand models

The increasing importance of logistics and its effects on transportation processes have been considered in freight transport demand modelling by new model developments incorporating several logistics aspects. However, transport logistics hubs – hubs without storage function that are primarily used for transhipment – seem to be not considered adequately in most freight models although they are crucial for freight transport. Currently it is not apparent which models do consider transport logistics hubs and how they do. The aim of this article is, therefore, to review how transport logistics hubs are considered in freight transport demand models and to discuss remaining gaps and challenges. Based on an extensive literature review a broad and internationally oriented analysis of transport models has been conducted. More than 100 models were examined regarding their consideration of logistics in general and transport logistics hubs in particular. The article points out the major importance of transport logistics hubs within freight transport and, thus, underpins the fact that they should also be considered sufficiently in freight demand models. It reveals that there are numerous models in application worldwide and that most of these models do not consider logistics or hubs. Only few models integrate logistics hubs, in general, and transport logistics hubs in particular. Furthermore, the consideration of transport logistics hubs within these models differs quantitatively (number and types of hubs) but also qualitatively (detail of hub integration) – also depending on model properties (e.g., scale of analysis, resolution or level of aggregation). Within freight transport processes, transport logistics hubs gained growing empirical significance in the recent past. This implies a major relevance to consider them in demand modelling, too. However, the article reveals that the integration of transport logistics hubs lags behind their empirical significance and that there are certain restrictions which hamper an adequate integration. The potential of many models is limited by reason of data availability. The lack of detailed data remains a major challenge that is, frankly, not new to the freight modelling community. Closing this data gap will help to consider transport hubs adequately and, thus, increase accuracy of models which would enable policy decision-makers to come to more effective decisions.

Commuting to college: The effectiveness and social efficiency of transportation demand management policies

Commuting is the single largest impact a University has on the environment and represents a noticeable share of urban traffic, when the University is located within a city. There is a large amount of literature on which policies could reduce car use and improve the environmental and social sustainability of commuting to college. However, most studies focus, to the best of our knowledge, only on the effectiveness of such policies, disregarding their social efficiency, measured as the difference between the social costs and benefits. This paper presents an estimate of the effectiveness and the efficiency of nine hypothetical transport policies regarding the University of Trieste, Italy, on the basis of a transport demand model estimated via revealed and stated choice data. All policies but one are effective in reducing car use, but only six of them appear to be efficient. We find that fully subsidizing bus fares would be the most effective and efficient policy. However, it is doubtful whether fully subsidizing bus fares is financially sustainable. The second best policy would be a mix of bus subsidies and parking restrictions. In case of the University of Trieste, our model suggests the adoption of a policy mix based on a relatively low hourly parking tariff (€0.3 per hour) and the use of the parking revenues to subsidize the bus users. The methodology and the results presented in this paper can be used by the college Mobility Managers to design better transport policies.

Opportunities of sectoral freight transport demand modelling

This paper discusses the opportunities of sectoral freight transport demand models. The work is based on literature and insights from interdisciplinary research in the field of production, logistics and transport. First, current and future factors influencing freight transport are discussed. Next, a brief summary of the traditional transport modelling approach and recent extensions and adaptations of freight transport models is given. As interdisciplinary research has shown, the impact of the identified factors on the development of freight transport is strongly dependent on the sector under investigation. As a consequence, this paper proposes the application of a sectoral modelling approach. The automotive and food sectors in Germany are used as examples to further examine the opportunities of sectoral freight transport demand models.

What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or other

The path most traveled: Travel demand estimation using big data resources

Rapid urbanization is placing increasing stress on already burdened transportation infrastructure. Ubiquitous mobile computing and the massive data it generates presents new opportunities to measure the demand for this infrastructure, diagnose problems, and plan for the future. However, before these benefits can be realized, methods and models must be updated to integrate these new data sources into existing urban and transportation planning frameworks for estimating travel demand and infrastructure usage. While recent work has made great progress extracting valid and useful measurements from new data resources, few present end-to-end solutions that transform and integrate raw, massive data into estimates of travel demand and infrastructure performance. Here we present a flexible, modular, and computationally efficient software system to fill this gap. Our system estimates multiple aspects of travel demand using call detail records (CDRs) from mobile phones in conjunction with open- and crowdsourced geospatial data, census records, and surveys. We bring together numerous existing and new algorithms to generate representative origin–destination matrices, route trips through road networks constructed using open and crowd-sourced data repositories, and perform analytics on the system’s output. We also present an online, interactive visualization platform to communicate these results to researchers, policy makers, and the public. We demonstrate the flexibility of this system by performing analyses on multiple cities around the globe. We hope this work will serve as unified and comprehensive guide to integrating new big data resources into customary transportation demand modeling.

Unreliability effects in public transport modelling

Nowadays, transport demand models do not explicitly evaluate the impacts of service reliability of transit. Service reliability of transit systems is adversely experienced by users, as it causes additional travel time and unsecure arrival times. Because of this, travellers are likely to perceive a higher utility from more reliable transport systems. In order to mimic and measure the impacts of service reliability on a transit demand model a three-step approach is proposed using automated vehicle location and smart card data. The approach consists of determining the probabilistic distribution of transit trip times, defining demand patterns and estimating the average impacts of unreliability per passenger. This approach was successfully tested on the model of the city of Utrecht in The Netherlands. By adding service reliability as a variable parameter of transit systems the results of the demand model improved, showing that the absolute difference between the observed and the estimated demand decreased by 18%. In addition, the proposed approach allows measuring the effects of expected changes in level of service reliability on traveller behaviour. Finally, we identify future research topics required to improve the estimation of those effects.

The Impact of Public Transport Network Accessibility on Trip Generation Model

The most commonly used model in transport planning is the four-step model of transport demand. Although a number of improvements have been made to this model over the past six decades of use, its main weakness remains that the characteristics of the transport network are not included in the sub-model of trip generation. In the research presented in this paper the authors investigated the possibility of improving this key model. Based on the results of correlation and regression analysis it has been proven that the public transport network accessibility significantly affects the total number of generated trips. This opens up new possibilities for improving this model as well as the process of transport planning.

A differentiated tolls system – an effective transport policy tool for Slovenia?

We provide an econometric model of passenger and freight transportation demand based on panel data for 10 sections of toll roads in Slovenia. Transportation demand on Slovenian motorways is price inelastic. The price elasticity of transportation demand statistically varies among different parts of the toll sections, so it would seem sensible to introduce a differentiated toll system that would allow the simultaneous achievement of multiple objectives, such as increasing the internalization of the external costs of transport, increasing transportation demand management on the basis of price and increasing cost recovery for the maintenance and development of toll roads in Slovenia. Transportation demand is even more responsive to changes in fuel price than changes in tolls, so transportation policy can manage transportation demand using differences in fuel prices. Based on estimates of demand models differentiated according to several geographical groups of road sections, we also find that growth in the price elasticity of transportation demand in Slovenia can be achieved through the expansion of the transport supply by increasing the competitiveness of rail transport and alternative transportation routes.

Integrating CEMDAP and MATSIM to Increase the Transferability of Transport Demand Models

An activity-based approach to transport demand modeling is considered the most behaviorally sound procedure to assess the effects of transport policies. This paper investigates whether it is possible to transfer an estimated model for activity generation from elsewhere (the estimation context) and use local area (application context) traffic counts to develop a local area activity-based transport demand representation. Here, the estimation context is the Los Angeles, California, area, and the application context is Berlin. Results in this paper suggest that such a transfer approach is feasible, according to a comparison with a Berlin travel survey. Additional studies need to be undertaken to examine the stability of the results obtained in this paper.

Short-Term Prediction of Ridership on Public Transport with Smart Card Data

Public transport operators are collecting massive amounts of data from smart card systems. In the Netherlands, every passenger checks in and checks out; this system creates detailed records of demand patterns. In buses and trams, users check in and check out in the vehicle; this factor provides good information on route choice. Options for analyzing smart card data and performing what-if analyses with transport planning software were explored. On the basis of big data, this new generation of transport demand models added to the existing range of transport demand models and approaches. The goal was to provide public transport operators with a simple (easy-to-build) model to perform what-if analyses. The data were converted to passengers per line and an origin–destination matrix between stops. This matrix was assigned to the network to repro-duce the measured passenger flows, and then what-if analysis became possible. With fixed demand, line changes could be investigated. With the introduction of an elastic demand model, changes in the level of service realistically affected passenger numbers. This method was applied to a case study in The Hague, Netherlands. Smart card data were imported into a transport model and connected with the network. The tool proved to be valuable to operators, who gained insights into the effects of small changes.

Applying Geovisualisation to Validate and Communicate Simulation Results of an Activity-based Travel Demand Model

Despite the immanently strong spatial relation of transport demand models ‒ both with respect to the input data, as well as the simulation results ‒ graphical, map-orientated visualisation with a focus on time-space-related phenomena is rarely applied for the purpose of validation, analysis, and communication of model data. At the same time, the visualisation of time-space related data is a prominent field of research in geography and geomatics, resulting in a wide range of examples for good representation. We present current work aiming at bridging this gap for the case of an activity-based travel demand model.