Visualisation of trip chaining behaviour and mode choice using household travel survey data

Wallner, Günter; Kriglstein, Simone; Chung, Edward; Kashfi, Syeed Anta

doi:10.1007/s12469-018-0183-5

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…In order to render the problem solvable in tractable time, the identification module was constrained to enumerate all tours whose total traveled distance did not exceed 95 km and the number of visited zones (i.e., legs) that did not exceed four. Numerous studies have showcased that the majority of tours do not include more than four legs (Wallner et al, 2018;Schneider et al, 2020). In addition, a brief analysis of the UK National Travel Survey 2016 (Department for Transport, 2017) indicated that only 0.5% of the recorded tours exhibited more than five legs, supporting the argument to limit the maximum number of legs in tours at this level.…”

Section: Parameters For the Identification Modulementioning

confidence: 99%

Exploitation of Aggregate Mobility Sensing Data for the Synthesis of Disaggregate Multimodal Tours in Megacities

Ballis

Dimitriou

2021

Front. Future Transp.

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The unprecedented volume of urban sensing data has allowed the tracking of individuals at remarkably high resolution. As an example, Telecommunication Service Providers (TSPs) cannot provide their service unless they continuously collect information regarding the location of their customers. In conjunction with appropriate post-processing methodologies, these traces can be augmented with additional dimensions such as the activity of the user or the transport mode used for the completion of journeys. However, justified privacy concerns have led to the enforcement of legal regulations aiming to hinder, if not entirely forbid, the use of such private information even for purely scientific purposes. One of the most widely applied methods for the communication of mobility information without raising anonymity concerns is the aggregation of trips in origin–destination (OD) matrices. Previous work has showcased the possibility to exploit multi-period and purpose-segmented ODs for the synthesis of realistic disaggregate tours. The current study extends this framework by incorporating the multimodality dimension into the framework. In particular, the study evaluates the potential of synthesizing multimodal, diurnal tours for the case where the available ODs are also segmented by the transport mode. In addition, the study proves the scalability of the method by evaluating its performance on a set of time period-, trip purpose-, and transport mode-segmented, large-scale ODs describing the mobility patterns for millions of citizens of the megacity of Tokyo, Japan. The resulting modeled tours utilized over 96% of the inputted trips and recreated the observed mobility traces with an accuracy exceeding 80%. The high accuracy of the framework establishes the potential to utilize privacy-safe, aggregate urban mobility data for the synthesis of highly informative and contextual disaggregate mobility information. Implications are significant since the creation of such granular mobility information from widely available data sources like aggregate ODs can prove particularly useful for deep explanatory analysis or for advanced transport modeling purposes (e.g., agent-based, microsimulation modeling).

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Section: Parameters For the Identification Modulementioning

confidence: 99%

Exploitation of Aggregate Mobility Sensing Data for the Synthesis of Disaggregate Multimodal Tours in Megacities

Ballis

Dimitriou

2021

Front. Future Transp.

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“…A few studies on the classification of trips by travel distance can be found in the literature. In the survey of urban residents' travel, the travel distance was subjectively divided into few distance segments, such as 0∼3 km, 3∼6 km, 6∼9 km, 9∼12 km, and longer than 12 km [33,34]. For mode split purpose, only qualitative classification of travel distance (short distance and long distance) was performed [35,36].…”

Section: Trip Length Classification To Test the Hypothesis Of Heterogenous Route Choice Behavior For Trips With Different Lengths The Akamentioning

confidence: 99%

Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data

Deng

Tang

et al. 2020

Journal of Advanced Transportation

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Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters’ route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi’an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters’ route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters’ route choice behaviors, when compared with the traditional MNL model.

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“…On the one hand, visualization techniques collect enormous amount of data (by applying e.g., Geographic Information Systems or sensors on the vehicles) on the movements of citizens then process and analyze the collected information (Wallner et al, 2018). Although an evident merit of this approach is a complete database on the actually conducted trips and number of passengers, the motivation of transport users or non-users remains uncovered.…”

Section: Introductionmentioning

confidence: 99%

Comparative mode choice analysis of university staff commuting travel preferences

Moslem,

Duleba,

Esztergár-Kiss

2022

European Journal of Transport and Infrastructure Research

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Surveying citizen preferences on transportation modes when commuting is a major issue in urban transport planning. Most of the current methods approach the problem through the attributes of choices thus forecasting the demand indirectly. This paper aims to analyze a survey of commuting students and university staff by two direct preference models: the Analytic Hierarchy Process and the Best-Worst Method. Both techniques are based on pairwise comparisons; consequently, the commuting transport alternatives can be directly compared with each other, and the results are comparable, too. However, the two methods differ in the number and the nature of comparisons and in the consistency check, thus they can be regarded as competitors. A real-world case study on commuting student groups provides a better understanding of the proposed methodology. As a result, it can be stated that despite their low utilization in the transportation field, both the Analytic Hierarchy Process and the Best-Worst Method are applicable to mode choice preference surveys, and they produce comprehensive final outcomes. Therefore, the well-known tools of mode choice can be extended by Multi-Criteria-Decision-Making techniques to increase the efficiency of transport demand prediction. The extension is beneficial to avoid the bias of other methods in converting attribute evaluations to real mode choice decision, as both models, especially the Best-Worst approach, requires less cost and time than the mainstream techniques.

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Visualisation of trip chaining behaviour and mode choice using household travel survey data

Cited by 10 publications

References 37 publications

Exploitation of Aggregate Mobility Sensing Data for the Synthesis of Disaggregate Multimodal Tours in Megacities

Exploitation of Aggregate Mobility Sensing Data for the Synthesis of Disaggregate Multimodal Tours in Megacities

Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data

Comparative mode choice analysis of university staff commuting travel preferences

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