Vol 18 No 1 (2018)
Vol 18 No 1 (2018)
Articles
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This special issue presents seven selected papers from the 5th Symposium of the European Association of Research in Transportation (hEART) which was held in September 2016 in Delft, The Netherlands, and was organised by Delft University of Technology. The contributions cover a wide range of topics in transportation, reflecting the broad scope of the hEART symposium series, including but not limited to travel behaviour, travel survey methods, railway freight as well as (terminal) logistics. As editors of this special issue we are very grateful for the efforts made by the authors, in particular their enduring efforts to push the quality of their contributions to a higher level during the review process. Also, we like to take the opportunity to thank all the reviewers for their constructive feedback and suggestions for improvement. This editorial provides a brief overview of the hEART2016 symposium. After that, an overview is given of the seven contributions that form this special issue.
This special issue presents seven selected papers from the 5th Symposium of the European Association of Research in Transportation (hEART) which was held in September 2016 in Delft, The Netherlands, and was organised by Delft University of Technology. The contributions cover a wide range of topics in transportation, reflecting the broad scope of the hEART symposium series, including but not limited to travel behaviour, travel survey methods, railway freight as well as (terminal) logistics. As editors of this special issue we are very grateful for the efforts made by the authors, in particular their enduring efforts to push the quality of their contributions to a higher level during the review process. Also, we like to take the opportunity to thank all the reviewers for their constructive feedback and suggestions for improvement. This editorial provides a brief overview of the hEART2016 symposium. After that, an overview is given of the seven contributions that form this special...
This special issue presents seven selected papers from the 5th Symposium of the European Association of Research in Transportation (hEART) which was held in September 2016 in Delft, The Netherlands, and was organised by Delft University of Technology. The contributions cover a wide range of...
Sander van Cranenburgh, Thijs Dekker -
This paper contributes to the research on non-reporting effects in mixed-method household travel surveys (HTS) in two ways: Firstly, we compare travel activities reported in the established Austrian National HTS (ANTS) with an innovative survey approach, the so-called “MobilityActivity-Expenditure Diary” (MAED), and secondly we extend the analysis to (i) additional travel estimates and to (ii) non-travel activities. The analysis addresses three main goals: (i) identification of non-reporting effects in the HTS for travel estimates, (ii) analysis of speed-ofresponse effects on travel estimates, (iii) assessment of the completeness and accuracy of nontravel activities inferred from the trip purposes in the HTS. Underreporting in HTS occurs both on person level and on the trip level, and mainly for peak-hour trips with either short distances or short durations of the subsequent non-travel activity. No significant underreporting was found on the tour level. Speed-of-response effects are small in both surveys but significant for the ANTS. The duration of non-travel activities per activity type corresponds well in the MAEDsurvey and in the ANTS but the information in the MAED-survey is much richer. The results can be used threefold: (i) to develop correction factors that account for systematic biases in HTS, (ii) to identify omitted items (trip frequency, duration, distance etc.) if HTS data are used without correction factors, and (iii) to demonstrate the importance of high quality field work and validation.
This paper contributes to the research on non-reporting effects in mixed-method household travel surveys (HTS) in two ways: Firstly, we compare travel activities reported in the established Austrian National HTS (ANTS) with an innovative survey approach, the so-called “MobilityActivity-Expenditure Diary” (MAED), and secondly we extend the analysis to (i) additional travel estimates and to (ii) non-travel activities. The analysis addresses three main goals: (i) identification of non-reporting effects in the HTS for travel estimates, (ii) analysis of speed-ofresponse effects on travel estimates, (iii) assessment of the completeness and accuracy of nontravel activities inferred from the trip purposes in the HTS. Underreporting in HTS occurs both on person level and on the trip level, and mainly for peak-hour trips with either short distances or short durations of the subsequent non-travel activity. No significant underreporting was found on the tour level. Speed-of-response...
This paper contributes to the research on non-reporting effects in mixed-method household travel surveys (HTS) in two ways: Firstly, we compare travel activities reported in the established Austrian National HTS (ANTS) with an innovative survey approach, the so-called...
Florian Aschauer, Reinhard Hössinger, Kay W. Axhausen, Basil Schmid, Regine Gerike -
This paper contributes to the literature on travel survey methods by quantifying the relationship between land use, data collection protocol and trip under-reporting in regional travel surveys. While under-reporting more broadly is a recognized problem, the significant increase in underreporting in denser, more urban-type environments identified here has never before been demonstrated or measured. Consequences of this land use-related bias for transportation planning and modelling are explored. The work is carried out by comparing the results of two very similar household travel surveys conducted in 2011 and 2013, in Toronto and Montreal respectively. Using data on over 350,000 persons, a binary logit model for discretionary trip making is estimated and the effects of land use and data collection protocol on under-reporting are isolated. This is done by controlling for mobility tool access, household type and other key determinants of travel demand. Counterintuitive effects for urban type environments found indicate the under-reporting effect is equivalent to a reduction in the pre-existing odds of reporting discretionary trip making in more urban environments of 19 to 29%. When combined with Toronto’s data collection protocol effect, the range increases to 39 to 55%. Results should be of use to transportation planning authorities wishing to make better use of the data collected in large surveys. Recognizing some of the flaws and biases in what is reported, these authorities can complement existing sources of data or modify their approaches to demandbased infrastructure provision to better account for the large number of, largely pedestrian, unreported trips.
This paper contributes to the literature on travel survey methods by quantifying the relationship between land use, data collection protocol and trip under-reporting in regional travel surveys. While under-reporting more broadly is a recognized problem, the significant increase in underreporting in denser, more urban-type environments identified here has never before been demonstrated or measured. Consequences of this land use-related bias for transportation planning and modelling are explored. The work is carried out by comparing the results of two very similar household travel surveys conducted in 2011 and 2013, in Toronto and Montreal respectively. Using data on over 350,000 persons, a binary logit model for discretionary trip making is estimated and the effects of land use and data collection protocol on under-reporting are isolated. This is done by controlling for mobility tool access, household type and other key determinants of travel demand. Counterintuitive effects for...
This paper contributes to the literature on travel survey methods by quantifying the relationship between land use, data collection protocol and trip under-reporting in regional travel surveys. While under-reporting more broadly is a recognized problem, the significant increase in...
Chris Harding, Monika Nasterska, Leila Dianat, Eric J Miller -
In this paper, the possibility and potential benefits of implementing discriminatory policies in rail freight transportation are analyzed, with the aim of revenue maximization. A regular, cyclic, single train service with fixed composition and capacity is studied. The problem is decomposed into discrete time periods. Transportation requests arise randomly over time, and the decision of either accept or reject a certain request has to be made. The problem is formulated via dynamic programming, and the deterministic approximations of the problem are used in order to formulate booking limits and bid price policies. Results obtained are compared with those of standard first come – first served policy, which is implemented by the Serbian railways. Although not acceptable in all contexts, the proposed aggressive policies demonstrated promising benefits.
In this paper, the possibility and potential benefits of implementing discriminatory policies in rail freight transportation are analyzed, with the aim of revenue maximization. A regular, cyclic, single train service with fixed composition and capacity is studied. The problem is decomposed into discrete time periods. Transportation requests arise randomly over time, and the decision of either accept or reject a certain request has to be made. The problem is formulated via dynamic programming, and the deterministic approximations of the problem are used in order to formulate booking limits and bid price policies. Results obtained are compared with those of standard first come – first served policy, which is implemented by the Serbian railways. Although not acceptable in all contexts, the proposed aggressive policies demonstrated promising benefits.
In this paper, the possibility and potential benefits of implementing discriminatory policies in rail freight transportation are analyzed, with the aim of revenue maximization. A regular, cyclic, single train service with fixed composition and capacity is studied. The problem is decomposed...
Marko Kapetanović, Nebojša Bojović, Miloš Milenković -
Workload forecasting related to truck arrivals is of essential importance not only for optimal resource allocation but also for encountering delays and bottlenecks in the landside operations of terminals. In order to increase their efficiency, some ports have tried to implement various methods such as Truck Appointment Systems (TAS) but, in general, information on the arrival times of trucks to pick up containers remains unreliable and scarce. This paper sought to develop pick-up time-of-day models for import containers using data easily retrieved from Terminal Operating Systems (TOS). Model results indicate that the receiver of goods and container characteristics are among the main factors affecting pick-up time-of-day. Differentiation is observed between the different days of the week. The developed Time-Of- Day (TOD) models can be used to calculate the probability of drayage truck arrival times. The application of the proposed methodology proves helpful when reliable information of truck arrivals is unavailable, and can also be used alongside TAS implementation to assist terminal operators.
Workload forecasting related to truck arrivals is of essential importance not only for optimal resource allocation but also for encountering delays and bottlenecks in the landside operations of terminals. In order to increase their efficiency, some ports have tried to implement various methods such as Truck Appointment Systems (TAS) but, in general, information on the arrival times of trucks to pick up containers remains unreliable and scarce. This paper sought to develop pick-up time-of-day models for import containers using data easily retrieved from Terminal Operating Systems (TOS). Model results indicate that the receiver of goods and container characteristics are among the main factors affecting pick-up time-of-day. Differentiation is observed between the different days of the week. The developed Time-Of- Day (TOD) models can be used to calculate the probability of drayage truck arrival times. The application of the proposed methodology proves helpful when reliable...
Workload forecasting related to truck arrivals is of essential importance not only for optimal resource allocation but also for encountering delays and bottlenecks in the landside operations of terminals. In order to increase their efficiency, some ports have tried to implement various methods...
Ioanna Kourounioti, Amalia Polydoropoulou -
In this work, we propose a Bayesian network approach by using structural restrictions and a model averaging algorithm for modeling the location choice of discretionary activities. In a first stage, we delimit individuals’ location choice which is set by generating an ellipse that uses empirical detour factors and a home-work axis. The choice set is further refined by an individual’s space-time constraints in order to identify the constrained destination choice set. We use structural restrictions and a model averaging method to learn the network structure of the Bayesian network in order to predict the heuristics of individuals’ location selection. The empirical study shows the proposed method can effectively obtain Bayesian networks with a consistent dependency structure. The empirical study suggests activity schedule factors significantly influence location choice decisions.
In this work, we propose a Bayesian network approach by using structural restrictions and a model averaging algorithm for modeling the location choice of discretionary activities. In a first stage, we delimit individuals’ location choice which is set by generating an ellipse that uses empirical detour factors and a home-work axis. The choice set is further refined by an individual’s space-time constraints in order to identify the constrained destination choice set. We use structural restrictions and a model averaging method to learn the network structure of the Bayesian network in order to predict the heuristics of individuals’ location selection. The empirical study shows the proposed method can effectively obtain Bayesian networks with a consistent dependency structure. The empirical study suggests activity schedule factors significantly influence location choice decisions.
In this work, we propose a Bayesian network approach by using structural restrictions and a model averaging algorithm for modeling the location choice of discretionary activities. In a first stage, we delimit individuals’ location choice which is set by generating an ellipse that uses...
Tai-Yu Ma, Sylvain Klein -
Households allocate their travel resources – vehicles, time, budget, and supervision – to accomplish activities while minimizing overall time and cost subject to a set of constraints – the duration and sequence of activities, and the need to provide transportation to dependent travellers. In this research, we develop and test a heuristic based approach to schedule activities using a cost (disutility) minimization objective. The model is evaluated by comparing predicted schedules generated by the heuristics to actual travel patterns reported by participant households. While the dataset is small – only 14 households are included – the model successfully identifies tours for households of various compositions and demographics. The research is important in the local context as the study area – the Region of Waterloo, Canada – is a largely auto-dependent metropolitan area that is building a 19km, $818M (CDN) Light Rail Transit (LRT) system intended to increase public transport use and influence land use change. The Region is amongst the smallest municipalities in North America to implement this infrastructure.
Households allocate their travel resources – vehicles, time, budget, and supervision – to accomplish activities while minimizing overall time and cost subject to a set of constraints – the duration and sequence of activities, and the need to provide transportation to dependent travellers. In this research, we develop and test a heuristic based approach to schedule activities using a cost (disutility) minimization objective. The model is evaluated by comparing predicted schedules generated by the heuristics to actual travel patterns reported by participant households. While the dataset is small – only 14 households are included – the model successfully identifies tours for households of various compositions and demographics. The research is important in the local context as the study area – the Region of Waterloo, Canada – is a largely auto-dependent metropolitan area that is building a 19km, $818M (CDN) Light Rail Transit (LRT) system intended to increase public...
Households allocate their travel resources – vehicles, time, budget, and supervision – to accomplish activities while minimizing overall time and cost subject to a set of constraints – the duration and sequence of activities, and the need to provide transportation to dependent...
Kevin Yeung, Jeffrey M. Casello -
Travel time reliability has attracted considerable interest in the field of route choice modelling. Knowing how individuals choose paths with uncertain travel times is fundamental to advancing our understanding of route choice behaviour and thus driving the development of route guidance systems. In general, existing navigation systems provide the shortest path on the basis of distance or travel time, even though many travellers do not intend to choose the shortest path. Several studies have shown that the probability of delay or travel time reliability is an important factor in a traveller’s route choice decision. Learning a traveller’s risk preference with regard to travel time reliability is important for designing a preferable route. Traditionally, route choice data for individual preference analysis are collected by conducting stated preference surveys. However, this approach is difficult to avoid its inherent limitation, namely a lack of honest, accurate, and bias-free reporting. To overcome these problems, the present study proposes a new data collection methodology that facilitates estimation of a traveller’s risk preference on the basis of large-scale GPS trip records. The lower and upper bounds of individual risk preference can be estimated by exhausting a series of reliable paths with different on-time arrival probabilities and using the theory of stochastic dominance. Then, a regression model based on a logistic function is established to explore how socio-demographic and trip characteristics influence the lower and upper bounds. Thus, individual properties, such as age, and pre-trip information, such origindestination (OD) distance, departure time, and day of week, are found to have a significant influence on the degree of risk preference.
Travel time reliability has attracted considerable interest in the field of route choice modelling. Knowing how individuals choose paths with uncertain travel times is fundamental to advancing our understanding of route choice behaviour and thus driving the development of route guidance systems. In general, existing navigation systems provide the shortest path on the basis of distance or travel time, even though many travellers do not intend to choose the shortest path. Several studies have shown that the probability of delay or travel time reliability is an important factor in a traveller’s route choice decision. Learning a traveller’s risk preference with regard to travel time reliability is important for designing a preferable route. Traditionally, route choice data for individual preference analysis are collected by conducting stated preference surveys. However, this approach is difficult to avoid its inherent limitation, namely a lack of honest, accurate, and bias-free...
Travel time reliability has attracted considerable interest in the field of route choice modelling. Knowing how individuals choose paths with uncertain travel times is fundamental to advancing our understanding of route choice behaviour and thus driving the development of route guidance...
Weiliang Zeng, Tomio Miwa, Takayuki Morikawa