An Alternative Approach to Network Demand Estimation: Implementation and Application in Multi-Agent Transport Simulation (MATSim)

Mtoi, Enock; Moses, Ren; Özgüven, Eren Erman

doi:10.1016/j.procs.2014.08.057

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Traffic flow conditions, weather information, information about detours, diversions, construction zones, accidents, and incidents have to be disseminated to drivers. Mtoi et al [34] and Min et al [35] indicate to new technologies, such as cameras with bright long-range night vision, drones, and also sensors enhance the system's ability to identify incidents and associated severity which ultimately leads to aiding the victim's life and alleviating traffic conditions to normalcy.…”

Section: Traffic Information System-implication For the Lusail Citymentioning

confidence: 99%

The Implementation of Smart Mobility for Smart Cities: A Case Study in Qatar

Tahmasseby

2022

Civ Eng J

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This paper contributes to building a systematic view of the mobility characteristics of smart cities by reviewing the lessons learned from the best practices implemented around the world. The main features of smart cities, such as smart homes, smart infrastructure, smart operations, smart services, smart utilities, smart energy, smart governance, smart lifestyle, smart business, and smart mobility in North America, Asia, and Europe are briefly reviewed. The study predominantly focuses on smart mobility features and their implications in newly built smart cities. As a case study, the modern city of Lusail located in the north of Doha, Qatar is considered. The provision of car park management and guidance, real-time traffic signal control, traffic information system, active-modes arrangement in promenade and busy urban avenues, LRT, buses, taxis, and water taxis information system, and multimodal journey planning facilities in the Lusail smart city is discussed in this study. Consequently, the implications of smart mobility features on adopting Intelligent Transportation Systems (ITS) will be studied. The study demonstrates that the implementation of Information and Communication Technologies (ICT) when supported by Intelligent Transportation Systems (ITS), could result in making the most efficient use of existing transportation infrastructure and consequently improve the safety and security, mobility, and the environment in urban areas. The findings of this study could be considered an initial step in the implementation of Mobility-as-a-Service (MaaS) in cities with advanced public transportation such as Doha, the capital of Qatar. Doi: 10.28991/CEJ-2022-08-10-09 Full Text: PDF

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Section: Traffic Information System-implication For the Lusail Citymentioning

confidence: 99%

The Implementation of Smart Mobility for Smart Cities: A Case Study in Qatar

Tahmasseby

2022

Civ Eng J

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“…Traffic accident detection and support systems integrate hardware, software, procedures, and facilities for real-time detection of traffic accidents and rapid assistance to the injured [4]. New technologies, such as cameras with better long-range night vision, sensors, and drones, can increase the system's ability to identify the context and severity of accidents, helping to reduce their impact on the victim's life and traffic conditions [55,56].…”

Section: Definitions Of Smart Mobility Authorsmentioning

confidence: 99%

“…[53,54,93,104,107].XXVI. Traffic accident detection and support system: A system formed by hardware, software, procedures, and facilities for real-time detection of traffic accidents and assistance to the injured [4,55,56,64,140]…”

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confidence: 99%

Smart Mobility: The Main Drivers for Increasing the Intelligence of Urban Mobility

et al. 2020

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Urban mobility plays a key role in the ecosystems of complex smart cities. It is considered a key factor in enabling cities to become more intelligent, which highlights the importance of identifying the drivers that improve the intelligence of cities. In this study, we investigate the main drivers with the potential to increase urban mobility intelligence and assign them a priority. Following on from a systematic review of the literature, we conducted broad and detailed bibliographic research based on the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). We also surveyed 181 professionals working in the field concerned to confirm the importance of different drivers and assign them a level of priority. The results show that 27 drivers identified in the literature were considered important, of which seven, related to city governance and technical solutions, were considered the most important to increase urban mobility intelligence.

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“…The mesoscopic method allows modelling more details with respect to the macroscopic model, by enabling the determination of individual trip times and waiting times. Mesoscopic simulations are slower compared with macroscopic assignments but still fast enough to simulate large urban areas [13][14][15][16]. Mesoscopic models are also used to determine a dynamic user equilibrium (DUE) by running simulations iteratively while updating link travel times [10].…”

Section: Introductionmentioning

confidence: 99%

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

Schweizer

Poliziani

Rupi

et al. 2021

IJGI

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A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

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An Alternative Approach to Network Demand Estimation: Implementation and Application in Multi-Agent Transport Simulation (MATSim)

Cited by 8 publications

References 11 publications

The Implementation of Smart Mobility for Smart Cities: A Case Study in Qatar

The Implementation of Smart Mobility for Smart Cities: A Case Study in Qatar

Smart Mobility: The Main Drivers for Increasing the Intelligence of Urban Mobility

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

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