GIS and Transport Modeling—Strengthening the Spatial Perspective

Loidl, Martin; Wallentin, Gudrun; Cyganski, Rita; Gräser, Axel; Scholz, Johannes; Haslauer, Eva

doi:10.3390/ijgi5060084

Cited by 51 publications

(37 citation statements)

References 114 publications

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“…Another multimodal transport model was developed by Dobler and Lämmel [51]. Their hybrid approach enabled the combination of macro-scaled demand models with micro-scaled, force-based, and agent-based models, where the latter was meant to represent active modes of transport (walking, cycling) [12].…”

Section: Microsimulation Modellingmentioning

confidence: 99%

Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

et al. 2019

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Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.

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Section: Microsimulation Modellingmentioning

confidence: 99%

Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

et al. 2019

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“…Static analytical methods of GIS ignore the dynamic nature of transportation, and microscopic transportation models have limited spatial transferability. In other words, the relevance of geospatial information for transport modeling is significant, but not yet adequately considered in most cases [28], which has even been unsettled in recent years. Thus, transportation models should be integrated with spatiotemporal GIS analysis techniques to accommodate the dynamics of traffic incidents.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

Zhang

et al. 2017

IJGI

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Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies.

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“…This parameter is used in: capacity analysis, level of service analysis, cost benefit analysis, safety analysis, analyses of pavement construction and for static calculation of road infrastructure objects, traffic forecasting, and others [18]. AADT presents the total volume of vehicle traffic of a highway or road for a year divided by 365 days (2). (2) The second problem we wanted to solve was to enable end-users on-line access, displaying and geolocation of traffic indicators, calculated and stored on Big Data platform.…”

Section: Problem Definitionmentioning

confidence: 99%

“…On the other hand, GIS and Big Data are two parts of a whole [1]. GIS tools search, sift and sieve data from multiple and disparate databases to organize it for better workflows and spatial analysis [2]. They run operations that aggregate terabytes and more spatial information, run analysis, and visualize results as maps.…”

Section: Introductionmentioning

confidence: 99%

Development of Traffic Geo-Application Based on Big Data Processing

Aćimović¹

2017

Proceedings of the International Scientific Conference - Sinteza 2017

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Abstract:Past is a prelude. Historical data provides signals that indicate what may happen in the future. Analytic tools can be especially useful in helping travel and transportation companies mine and refine data to determine which information is valuable for optimizing business outcomes. In this research we define a methodology for developing traffic geo-applications, based on: 1) relational data model for calculated predefined attributes of the maps, 2) wide column data model for source traffic data store, and 3) schema on read modeling approach for traffic data analysis, ie. calculating of the attributes of the maps. According to the proposed methodology we have developed Traffic Counting geo-application, which enables on-line access, displaying and geo-location of traffic indicators, calculated and stored on Apache Hadoop database. We concluded that our approach is appropriate in scenarios that involve batch Big Data processing and predefined data analysis, but not appropriate for the on-line ad-hoc definition of arbitrary queries.

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GIS and Transport Modeling—Strengthening the Spatial Perspective

Cited by 51 publications

References 114 publications

Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

Development of Traffic Geo-Application Based on Big Data Processing

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