Search

People’s behavior is governed by extremely complex, multidimensional processes. This fact is well-established in the transportation research community, which has been working on travel behavior (travel demand) models for many years. The number of degrees of freedom in a person’s activity schedule is enormous. However, the frequency of occurrence of day-long activity schedules obeys a remarkably simple, scale-free distribution. This particular distribution has been observed in many natural and social processes and is commonly referred to as Zipf’s law, a power law distribution. This research provides evidence that activity schedules from various study areas exhibit a universal power law distribution. To this end, an elaborate analysis using 13 household travel surveys from diverse study areas discusses the efect of proportional outlier removal on the power law’s exponent value. Statistical evidence is provided for the hypothesis that activity schedules in all these datasets exhibit a power law distribution with a common exponent value. The study proposes that a Zipf power law could be used as an additional dimension within a travel demand model’s validation process. Contrary to other validation methods, no new data is required. The observation of a Zipf power law distribution in the generated schedules appears to be a necessary condition. Additionally, the universal activity schedule distribution might enable the full integration of activity schedules in models based on universal mobility patterns. The authors are deeply grateful to the original data creators, depositors and/or copyright holders for making the microdata available for this research. The copyright and all other intellectual property rights in the data and associated documentation are vested in the original data creators or depositors. The original data creators and analyzers bear no responsibility for the further analysis or interpretation of the data in this research. The authors would like to acknowledge the researches, works, individuals and institutions supporting the following data collections for making this research possible: US NHTS 2009 (US Department of Transportation and Federal Highway Administration 2009), NLD OViN 2013 (Centraal Bureau voor de Statistiek (CBS) and Rijkswaterstaat (RWS) 2014), BEL OVG 3.0--4.5 (Janssens et al. 2014), SVN Ljubljana 2013 (Klemencic et al. 2014) [Acknowledgements go out to the City Municipality of Ljubljana], GBR NTS 2009--2014 (Department for Transport 2015), KOR Seoul HTS 2010 (Korea Transportation Institute 2011; Metropolitan Transport Authority 2012), DEU Mobidrive 1999 (Chalasani and Axhausen 2004), CHE Thurgau 2003 (Loechl 2005), FRA ENTD 2008 (Armoogum et al. 2011), BEL Beldam 2010 (Cornelis et al. 2012) [financed by BELSPO, FOD Mobiliteit & Vervoer and others. Coordinated by GRT (Universite de Namur) in cooperation with IMOB (Universiteit Hasselt) and CES (FUSL)], IRL NTS 2009 (Central Statistics Office 2011) [Accessed via the Irish Social Science Data Archive -www.ucd.ie/issda], FIN HLT 2010--2011 (Liikennevirasto -Finnish Transport Agency) [Finnish National Travel Survey 2010--2011/Finnish Transport Agency and Wim Ectors], SWE RVU 2011--2014 (Trafik Analys 2015), AUS VISTA 2007 & 2009 (Department of Economic Development; Jobs; Transport and Resources (DEDJTR) 2009; Department of Economic Development; Jobs; Transport and Resources (DEDJTR) 2007). Part of this work was presented at the hEART 2016 conference in Delft, The Netherlands, offering valuable reflections during the preparation of this work. (Ectors et al. 2016a)

This paper describes the design of SimPark, an agent-based micro-simulator with state-of-the-art parking components. The goal is to allow the simulation of parking policies and strategies while capturing their effects on traffic flow and personal schedules. Though individuals’ parking strategies are an important part of the simulator, they are out of scope in the presented research. All other components of the simulator are discussed in depth and compared with existing parking simulators.

Recently, multirotor Unmanned Aerial Vehicles(UAVs) or drones have become increasingly popular for a vast variety of civil applications. Efficient traffic data collection and extraction of various flow parameters are some of the futuristic applications of this technology. However, such applications still need to be streamlined and thoroughly explored for varying traffic and infrastructural conditions. In this paper, the focus is on the authentication of the application of small multirotor UAVs for traffic data collection and subsequent analysis of traffic streams at urban roundabouts. This paper presents an analytical methodology to evaluate the performance of roundabouts by extracting various parameters and performance indicators. The performance evaluation methodology is based on: (i) determining traffic volume via OD matrices for each leg, and (ii) analyzing drivers’ behavior via gap-acceptance analysis. The overall analytical process is principally based on the authors’ previously proposed automated UAV video-processing framework for the extraction of vehicle trajectories. The extracted trajectories are further employed to extract useful traffic information. The experimental data to analyse roundabout traffic flow conditions was obtained in the city of Sint-Truiden (Belgium). The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. With the significant increase in the usage of UAVs expected in the coming years, such studies could become a useful resource for practitioners as well as future researchers. The future research will mainly focus on further extensions of the UAV-based traffic applications.

The generation of substantial amounts of travel- and mobility-related data has spawned the emergence of the era of big data. However, this data generally lacks activity-travel information such as trip purpose. This deficiency led to the development of trip purpose inference (activity type imputation/annotation) techniques, of which the performance depends on the available input data and the (number of) activity type classes to infer. Aggregating activity types strongly increases the inference accuracy and is usually left to the discretion of the researcher. As this is open for interpretation, it undermines the reported inference accuracy. This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set. A sensitivity analysis was performed. Additionally, several machine learning algorithms are experimented with. The proposed method may be applied to any study area. This work was supported by the National Research Foundation of Korea funded by the Korean government (MSIP) under grant NRF-2010-0028693.

Carpooling enables commuters to share travel expenses, save costs, and improve their mobility options and reduces emission and traffic congestion. To commute by carpooling, individuals need to communicate, negotiate, and coordinate, and in most cases they need to adapt their schedule to enable cooperation. This paper presents the design of an agent-based model by defining phases and steps that may be taken to move from solo driving to carpooling. The paper analyzes the various effects of agent interaction and behavior adaptation for a set of candidate carpoolers. The start of the carpooling process depends on the individuals’ objectives and intention to carpool. Through negotiation and coordination, individuals can reach complex agreements in an iterative way. The success of negotiation highly depends on the lifestyle factors that influence the departure time decision, on the profile of the individuals, and on the effect of constraining activities. The carpooling social network was established by use of the results predicted by FEATHERS, an operational activity-based model for Flanders, Belgium. From the simulation’s discussions, it is possible to portray the true picture of potential carpoolers throughout their carpooling period. The simulation results show that 9.33% of the commuters started to carpool when the time window was ±30 min and the average occupancy per car was 2.4 persons. When the time window was larger, the chances for negotiation success were greater than those when a smaller time window was used. Hence, carpooling requires time flexibility. The Janus (multiagent) platform was used to simulate the interactions of autonomous agents.

Currently walking is a multidisciplinary and emerging point of attention for urban sustainability and for ensuring the quality of pedestrian environments. In order to understand pedestrian behaviour, walkability researches estimate the factors which affect the level of pedestrian satisfaction. Past studies focused on the relationship between environmental factors and pedestrian behavioural outcomes. In this study, we developed pedestrian satisfaction multinomial logit models using various data sets, examining the relative impact of five differently themed sets of attributes: personal, walk-facilities, land-use, pedestrian volumes, and weather-related variables. The results show that the personal variability attributes were selected as the most significant. We investigated the effects of personal variability, such as the spatial cognition level and travel purpose, and detailed effects of environmental features. In addition, crowdedness, land-use types, and residential information were investigated. The results from this study offer contributions by providing evidence of the importance of personal and contextual variables in influencing pedestrian walkability. This work was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIP) (NRF-2010-0028693).

When simulating individuals' daily plan, lots of unexpected events need to be considered, like traffic jam and weather changes. Therefore, there will be a mismatch between the original plan and the executed one. Faced with this situation, individuals need to adjust the rest of the activities to make a new schedule. This paper analyzes the causes of rescheduling, and establishes a new rescheduling model, combining strengths of existing rescheduling models. The model in this paper considers the rescheduling possibilities and choices as much as possible. It takes time pressure and schedule similarity into consideration when updating a schedule. Furthermore, this paper analyzes joint trip/activity execution by studying the cooperation between agents during the rescheduling process. (C) 2018 The Authors. Published by Elsevier B.V.

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting over the last few decades. FEATHERS (The Forecasting Evolutionary Activity-Travel of Households and their Environmental Repercussions), developed by the Transportation Research Institute of Hasselt University, Belgium, is a micro-simulation framework developed to facilitate the implementation of activity-based models for transport demand forecasting. In this paper, we focus on several model considerations when applying this framework. First, the way to apply FEATHERS on a more disaggregated geographical level is investigated, with the purpose of obtaining more detailed travel demand information. Next, to reduce the computation time when applying FEATHERS on a more detailed geographical level, an iteration approach is proposed to identify the minimum size of the study area needed. In addition, the effect of stochastic errors inherently included in the FEATHERS framework is investigated, and the concept of confidence intervals is applied to determine the minimum number of model runs needed to minimize this effect. In the application, the FEATHERS framework is used to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders, Belgium. In doing so, all the aforementioned model considerations are taken into account. The results indicate that by integrating a light rail network into the current public transport network, there would be a relatively positive impact on public transport-related trips, but a relatively negative impact on the non-motorized-mode trips in this area. However, no significant change is found for car-related trips.

People’s behavior depends on extremely complex, multidimensional processes. This poses challenges when trying to model their behavior. In the transportation modeling community, great effort is spent to model the activity schedules of people. Remarkably however, the frequency of occurrence of day-long activity schedules obeys a ubiquitous power law distribution, commonly referred to as Zipf’s law. Previous research established the universal nature of this distribution and proposed potential application areas. However, these application areas require additional information about the distribution’s properties. To stress-test this universal power law, this paper discusses the role of aggregation within the phenomenon of Zipf’s law in activity schedules. Aggregation is analyzed in three dimensions: activity type encoding, aggregation over time and the aggregation of individual data. Five data sets are used: the household travel survey from the USA (2009) and from GBR (2009–2014), two six-week travel surveys (DEU MobiDrive 1999 and CHE Thurgau 2003) and a donated 450-day data set from one individual. To analyze the effect of aggregation in the first dimension, five different activity encoding aggregation levels were created, each aggregating the activity types somewhat differently. In the second dimension, the distribution of schedules is compared over multiple years and over the days of the week. Finally, in the third dimension, the analysis moves from study area-wide aggregated data to subsets of the data, and finally to individual (longitudinal) data.

Demand responsive collective transportation might be a solution to serve thin flows that occur when the average demand per time unit for travel between particular locations is small. Small capacity and/or specially equipped vehicles are deployed to serve low population density areas and mobility impaired passengers. The variability of the demand and the low vehicle capacity require daily optimal planning of routes. The cost for daily tours heavily depends on the temporal and spatial distribution of the demand. This paper proposes a co-simulation model to evaluate the profitability of thin flow service providers over multiple years of operation under specific condition of public compensation (subsidizing). The research reported was partially co-funded within ERA-NET Transport III Flagship Call 2013 “Future Traveling”, project: “Smart-PT - Smart Adaptive Public Transport”, under grant IWT 135026 (Belgium) and NCBR ERA-NET-TRANSPORT-III/2/2014 (Poland).

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.