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See detailIdentifying the Determinants of Light Rail Mode Choice for Medium- and Long-Distance Trips: Results from a Stated Preference Study
Creemers, Lieve; Cools, Mario ULg; Tormans, Hans et al

in Transportation Research Record: Journal of the Transportation Research Board (2012), 2275

he introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of ... [more ▼]

he introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of light rail mode choice for medium- and long-distance trips (10 to 40 km) for a new light rail system in Flanders, Belgium. To investigate these choices, the effects of various transport system-specific factors (i.e., travel cost, in-vehicle travel time, transit punctuality, waiting time, access and egress time, transfers, and availability of seats) as well as the travelers' personal traits were analyzed by using an alternating logistic regression model, which explicitly takes into account the correlated responses for binary data. The data used for the analysis stem from a stated preference survey conducted in Flanders. The modeling results are in line with literature: most transport system-specific factors as well as socioeconomic variables, attitudinal factors, perceptions, and the frequency of using public transport contribute significantly to the preference for light rail transit. In particular, the results indicate that the use of light rail is strongly influenced by travel cost and in-vehicle travel time and to a lesser extent by waiting and access-egress time. Seat availability appeared to play a more important role than did transfers in deciding to choose light rail transit. The findings of this paper can be used by policy makers as a frame of reference to make light rail transit more successful. [less ▲]

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See detailOptimizing the implementation of policy measures through social acceptance segmentation
Cools, Mario ULg; Brijs, Kris; Tormans, Hans et al

in Transport Policy (2012), 22

This paper proposes Q-methodology as a technique for the identification of more homogeneous subgroups or ‘segments’ within a rather heterogeneous overall population when it comes to social acceptance of ... [more ▼]

This paper proposes Q-methodology as a technique for the identification of more homogeneous subgroups or ‘segments’ within a rather heterogeneous overall population when it comes to social acceptance of demand-restricting policy measures. Identification of such segments would allow policy makers to better tailor their future actions and thereby increase the chance for a successful implementation of the measures they propose. A set of 33 persons, selected in function of age, gender and car ownership evaluated the acceptability of a total number of 42 demand-restricting policy measures. Special care was taken that the final set of statements covered the four classically distinguished demand-restricting strategies, i.e., improved transport options, incentives for the use of alternative transport modes, parking and land-use management, and institutional policy revision. In addition, a balance between both ‘hard’ and ‘soft’ and ‘push’ and ‘pull’ measures was strived for. The results indicate that four different segments in terms of social acceptance of demand-restricting policy measures can be distinguished, i.e., travelers in favor of traffic calming, travelers against hard push measures, travelers in favor of demand restriction, and travelers against policy innovations. Besides the differences and similarities between these segments, the practical implications for policy makers are discussed, together with a series of specific recommendations and suggestions for future research. [less ▲]

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See detailA Bayesian approach for modeling origin–destination matrices
Perrakis, Konstantinos; Karlis, Dimitris; Cools, Mario ULg et al

in Transportation Research. Part A : Policy & Practice (2012), 46(1), 200212

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel ... [more ▼]

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis–Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows. [less ▲]

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See detailOnderzoek Verplaatsingsgedrag Vlaanderen 4.2 (2009-2010): Analyserapport
Janssens, Davy; Cools, Mario ULg; Miermans, Willy et al

Report (2011)

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See detailOnderzoek Verplaatsingsgedrag Vlaanderen 4.2 (2009-2010): Tabellenrapport
Cools, Mario ULg; Declercq, Katrien; Janssens, Davy et al

Report (2011)

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See detailAvoiding congestion in freight transport planning: a case study in Flanders
Caris, An; Cools, Mario ULg; Debels, Dieter

Conference (2011)

A substantial increase in transport intensity for passenger and freight traffic has been observed during the last decades and research confirms that this trend will continue in the years to come. Economic ... [more ▼]

A substantial increase in transport intensity for passenger and freight traffic has been observed during the last decades and research confirms that this trend will continue in the years to come. Economic centres have turned into heavily congested areas. The freight transport sector incurs excessive waiting times on the road as well as at intermediate stops (e.g. sea terminals, loading or unloading points). This may cause economic losses and environmental damages. Waiting times may be avoided by taking into account congestion in freight transport planning. Vehicle routing problems arise when several pickup and delivery operations need to be performed, mainly by truck, over relatively short distances [1]. Congestion leads to uncertain travel times on links and uncertain waiting times at pickup or delivery locations. Peak hours may be avoided on congested road segments by changing the order in which customers are served. On the other hand, time slots at customer sites may be renegotiated, creating more flexibility to avoid congestion on the road and at customer stops. The objective of this paper is to estimate the benefits of taking congestion into account in transport planning and to quantify the impact of delivery restrictions on transport costs. A highly congested road network raises the need for robust vehicle routing decisions. Current traffic conditions give rise to uncertain travel times. The reliability of travel time on a route is one of the dominant factors affecting route and departure time choices in passenger transport [2]. Similarly, in freight transport the reliability of travel times may be taken into account when planning vehicle routes. In this paper congestion is modelled as time-dependent travel times. These travel times take into account the dynamics of the time lost due to congestion using the Bureau of Public Roads (BPR) function, which is commonly-used for relating travel times to increases in travel volume [3]. The Time Dependent Vehicle Routing Problem (TDVRP) will be studied as a deterministic planning problem taking into account peak hour traffic congestion. Solution methods for the TDVRP have been focused on heuristic approaches [4, 5, 6, 7]. Kok [8] applies a restricted dynamic programming heuristic to solve a TDVRP. In this paper a heuristic algorithm will be presented to solve problem instances of realistic size. Next, this algorithm will be applied to perform a sensitivity analysis to identify which congestion avoiding strategies have a large influence on the objective function. Shippers may adapt the way they plan their transport as a strategy to avoid congestion. For example, time windows at customer locations may be renegotiated, departure times at the depot may be questioned or the assignment of customers to routes and the order in which customers are served may be changed. The proposed methodology will be demonstrated with a Flemish case study. [less ▲]

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See detailThe dual role of weather forecasts on changes in activity-travel behavior
Cools, Mario ULg; Creemers, Lieve; Janssens, Davy et al

Conference (2011)

In previous studies, conflicting results could be found regarding the impact of weather forecasts on travel decisions, e.g. Khattak and De Palma (1997) found no significant effect of acquiring forecasted ... [more ▼]

In previous studies, conflicting results could be found regarding the impact of weather forecasts on travel decisions, e.g. Khattak and De Palma (1997) found no significant effect of acquiring forecasted weather information on the probability of adapting mode and departure time, whereas the results reported by Hagens (2005), Niina (2009) and Kilpelainen and Summala (2007) indicated that weather forecast do play an important role. Therefore this paper investigates the changes in activity-travel behavior in response to weather forecasts. The data for this study is collected by means of a stated adaptation survey, which is both administered on the internet and via a traditional paper and pencil questionnaire. In total, 595 respondents completed the survey. To obtain an optimal correspondence between the true population and the sample weights are assigned to the observation. Results indicate that weather information plays a dual role. On the one hand people do alter their activity-travel behavior in response to weather information, albeit these changes are not as pronounced when compared to actual weather. On the other hand the extent (frequency and media type) to which people are exposed to these weather forecasts appears to play only a marginal role. This dual role weather information plays in this study appears to be supported by the conflicting international literature and therefore revealing the underlying psychological motivations to change one's activity-travel behavior is a key challenge for further research. [less ▲]

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See detailA Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
Yang, Banghua; Janssens, Davy; Ruan, Da et al

in Wang, Y.; Li, T. (Eds.) Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE 2011) (2011)

In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the ... [more ▼]

In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the missing elements of ‘number of cars’ and ‘driver license’. The inputs of the former SVM model include five variables (Household composition, household income, Age oldest household member, Children age class and Number of household members). The inputs of the latter SVM model include three variables (personal age, work status and gender). The SVM models to predict the ‘number of cars’ and ‘driver license’ can achieve accuracies of 69% and 83% respectively. The initial experimental results show that missing elements of observed activity diaries can be accurately inferred by relating different pieces of information. Therefore, the proposed SVM data imputation method serves as an effective data imputation method in the case of missing information. [less ▲]

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See detailAn estimation of total vehicle travel reduction in the case of telecommuting. Detailed analyses using an activity-based modeling approach.
Kochan, Bruno; Bellemans, Tom; Cools, Mario ULg et al

in Proceedings of the 39th European Transport Conference (2011)

Transportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the potential measures that can be ... [more ▼]

Transportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the potential measures that can be adopted in TDM is the implementation of telecommuting. A significant number of studies have been conducted in the past to evaluate the effect of telecommuting on the amount of peak-period trips. However it is less studied whether telecommuting also effectively and significantly reduces total vehicle travel in terms of kilometers traveled throughout the day. For this reason, a conventional modeling approach was adopted in this paper to calculate total kilometers of travel saved in the case telecommuting would materialize in the Flanders area. In a second part, this paper introduces the use of an activity-based modeling approach to evaluate the effect of telecommuting on a more detailed time scale. As the second approach provides a more disaggregate result, both models can be compared on the more aggregate level to validate whether they correspond. [less ▲]

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See detailAn estimation of total vehicle travel reduction in the case of telecommuting. Detailed analysis using an activity-based modeling approach
Kochan, Bruno; Bellemans, Tom; Cools, Mario ULg et al

in Cornelis, Eric (Ed.) Proceedings of the BIVEC-GIBET Transport Research Day 2011 (2011)

ransportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the possible measures that can be ... [more ▼]

ransportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the possible measures that can be adopted in TDM is the implementation of telecommuting. A significant number of studies have been conducted in the past to evaluate the effect of telecommuting on peak-period trips. However it is less studied whether telecommuting also effectively and significantly reduces total vehicle travel. For this reason, a conventional modeling approach was adopted in this paper to calculate total kilometers of travel saved in the case telecommuting would materialize in the Flanders area. In a second part, the paper also introduces the use of an activity-based modeling approach to evaluate the effect of telecommuting. By doing so, an operational activity-based framework is externally validated by means of another completely different model, both calibrated for the same application and study area. [less ▲]

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See detailA Bayesian Approach for Modeling Origin-Destination Matrices
Perrakis, Konstantinos; Karlis, Dimitris; Cools, Mario ULg et al

in Proceedings of the 90th Annual Meeting of the Transportation Research Board (DVD-ROM) (2011)

The majority of Origin Destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel ... [more ▼]

The majority of Origin Destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows, derived from census data, is related only to a set of general explanatory variables. The assumptions of a Poisson model and of a Negative-Binomial model are investigated on a realistic application area concerning the region of Flanders on the level of municipalities. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo algorithm, known as the Metropolis-Hastings algorithm. Additionally, a strategy is proposed in order to obtain predictions from the hierarchical, Poisson-Gamma structure of the Negative-Binomial model conditional on the posterior expectations of the mixing parameters. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows. Predictive goodness-of-fit tests suggest a good fit to the data and overall results indicate that the approach is applicable on large networks, with relatively low computational and explanatory data-gathering costs. [less ▲]

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See detailModeling Route Choice of Car Travelers using an Activity-Based Segmentation
Cools, Mario ULg; Ramaekers, Katrien; Reumers, Sofie et al

in Proceedings of the 90th Annual Meeting of the Transportation Research Board (DVD-ROM) (2011)

The aim of this research is to identify the relationships between activity patterns and route choice decisions. The focus is turned to the relationship between the purpose of a trip and the road ... [more ▼]

The aim of this research is to identify the relationships between activity patterns and route choice decisions. The focus is turned to the relationship between the purpose of a trip and the road categories used for the relocation. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between he primary road category traveled on and the corresponding activity-travel behavior a multinomial logit model is developed. The results point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modeling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. This certainly suggests that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers. A potential pathway for further investigating route choice decisions might lie in the roots of more psychological underpinnings. [less ▲]

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See detailAssessment of the Effect of Micro-Simulation Error on Key Travel Indices: Evidence from the Activity-Based Model FEATHERS
Cools, Mario ULg; Kochan, Bruno; Bellemans, Tom et al

in Proceedings of the 90th Annual Meeting of the Transportation Research Board (DVD-ROM) (2011)

Current transportation models often do not explicitly address the degree of uncertainty in travel forecasts. Of particular interest in activity-based travel demand models is the model uncertainty that is ... [more ▼]

Current transportation models often do not explicitly address the degree of uncertainty in travel forecasts. Of particular interest in activity-based travel demand models is the model uncertainty that is caused by the statistical distributions of random components, i.e. micro-simulation error. Therefore, the main objective of this paper is to assess the impact of micro-simulation error on two key travel indices, namely the average daily number of trips per person and the average daily distance traveled per person. The effect of micro-simulation error will be investigated by running the activity-based modeling framework FEATHERS 200 times using the same 10% fraction of the population. Results show that micro-simulation errors are limited especially when disaggregation is limited to two levels. Notwithstanding, results indicate that for more elaborate analyses a 10% fraction might not be sufficient. The size of micro-simulation error increases along with complexity. Moreover, more commonly used transport modes such as using the car as driver have a lower error rate. Further research should investigate the impact of the population fraction on the micro-simulation error rates. Besides, one could also investigate other aspects (e.g. the number of activities) involved in the activity-scheduling process. [less ▲]

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See detailImproved Policy Support Through Segmentation Based on Social Acceptance
Cools, Mario ULg; Brijs, Kris; Tormans, Hans et al

in Proceedings of the 90th Annual Meeting of the Transportation Research Board (DVD-ROM) (2011)

This paper proposes Q-methodology as a technique for the identification of more homogeneous subgroups or ‘segments’ within a rather heterogeneous overall population when it comes to social acceptance of ... [more ▼]

This paper proposes Q-methodology as a technique for the identification of more homogeneous subgroups or ‘segments’ within a rather heterogeneous overall population when it comes to social acceptance of demand restricting policy measures. Identification of such segments would allow policy makers to better tailor their future actions and thereby increase the chance for a successful implementation of the measures they propose. A set of 33 persons, selected in function of age, gender and car ownership evaluated the acceptability of a total number of 42 demand restricting policy measures. Special care was taken that the final set of statements covered the four classically distinguished demand restricting strategies, i.e., improved transport options, incentives for the use of alternative transport modes, parking and land-use management, and institutional policy revision. In addition, a balance between both ‘hard’ and ‘soft’ and ‘push’ and ‘pull’ measures was strived for. The results indicate that four different segments in terms of social acceptance of demand restricting policy measures, can be distinguished, i.e., travelers in favor of traffic calming, travelers against hard push measures, travelers in favor of demand restriction, and travelers against policy innovations. Besides the differences and similarities between these segments, the practical implications for policy makers are discussed, together with a series of specific recommendations and suggestions for future research. [less ▲]

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See detailThe socio-cognitive links between road pricing acceptability and changes in travel-behavior
Cools, Mario ULg; Brijs, Kris; Tormans, Hans et al

in Transportation Research. Part A : Policy & Practice (2011), 45(8), 779-788

The objective of this study is to examine the effect of road pricing on people’s tendency to adapt their current travel behavior. To this end, the relationship between changes in activity-travel behavior ... [more ▼]

The objective of this study is to examine the effect of road pricing on people’s tendency to adapt their current travel behavior. To this end, the relationship between changes in activity-travel behavior on the one hand and public acceptability and its most important determinants on the other are investigated by means of a stated adaptation experiment. Using a two-stage hierarchical model, it was found that behavioral changes themselves are not dependent on the perceived acceptability of road pricing itself, and that only a small amount of the variability in the behavioral changes were explained by socio-cognitive factors. The lesson for policy makers is that road pricing charges must surpass a minimum threshold in order to entice changes in activity-travel behavior and that the benefits of road pricing should be clearly communicated, taking into account the needs and abilities of different types of travelers. Secondly, earlier findings concerning the acceptability of push measures were validated, supporting transferability of results. In line with other studies, effectiveness, fairness and personal norm all had a significant direct impact on perceived acceptability. Finally, the relevance of using latent factors rather than aggregate indicators was underlined. [less ▲]

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See detailOnderzoek Verplaatsingsgedrag Vlaanderen 4.1 (2008-2009): Verkeerskundige interpretatie van de belangrijkste gegevens
Miermans, Willy; Janssens, Davy; Cools, Mario ULg et al

Report (2010)

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See detailAn integrated micro-simulation modeling framework to measure and predict emissions and dynamic exposure
Janssens, Davy; Cools, Mario ULg; Vanhoof, Koen et al

Conference (2010)

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See detailAssessing the Quality of Origin-Destination Matrices Derived from Activity Travel Surveys: Results from a Monte Carlo Experiment
Cools, Mario ULg; Moons, Elke; Wets, Geert

in Transportation Research Record: Journal of the Transportation Research Board (2010), 2183

To support policy makers combating travel-related externalities, quality data are required for the design and management of transportation systems and policies. To this end, much money has been spent on ... [more ▼]

To support policy makers combating travel-related externalities, quality data are required for the design and management of transportation systems and policies. To this end, much money has been spent on collecting household- and person-based data. The main objective of this paper is to assess the quality of origin-destination (O-D) matrices derived from household activity travel surveys. To this purpose, a Monte Carlo experiment is set up to estimate the precision of O-D matrices given different sampling rates. The Belgian 2001 census data, containing work- and school-related travel information for all 10,296,350 residents, are used for the experiment. For different sampling rates, 2,000 random stratified samples are drawn. For each sample, three O-D matrices are composed: one at the municipality level, one at the district level, and one at the provincial level. The correspondence between the samples and the population is assessed by using the mean absolute percentage error (MAPE) and a censored version of the MAPE (MCAPE). The results show that no accurate O-D matrices can be derived directly from these surveys. Only when half of the population is queried is an acceptable O-D matrix obtained at the provincial level. Therefore, use of additional information to grasp better the behavioral realism underlying destination choices and collection of information about particular O-D pairs by means of vehicle intercept surveys are recommended. In addition, results suggest using the MCAPE next to traditional criteria to examine dissimilarities between different O-D matrices. An important avenue for further research is the investigation of the effect of sampling proportions on travel demand model outcomes. [less ▲]

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