Development and Application of an Evaluation Framework for Urban Traffic Management and Intelligent Transport Systems

Kaparias, Ioannis; Eden, Niv; Tsakarestos, Antonios; Gal‐Tzur, Ayelet; Gerstenberger, Marcus; Hoadley, Suzanne; Lefebvre, Patrick; Ledoux, Justin; Bell, M.G.

doi:10.1016/j.sbspro.2012.06.1276

Cited by 19 publications

(14 citation statements)

References 4 publications

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“…Kaparias et al [15] define mobility as the key element of the transport system i.e. as the capability of the transport system to ensure access to work, recreation, commerce, the intermodal transfer points and other functions.…”

Section: Related Data Sources and Datamentioning

confidence: 99%

“…The final list is narrowed down to 24 indicators. Within the Conduits project [15,20], the authors Kaparis and Bell have defined a set of indicators Table 2 -An overview of the transport indicators [4,6,7,15,20,[47][48][49]…”

Section: Literature Reviewmentioning

confidence: 99%

“…Cilj ovog rada bio je provesti opsežna istraživanja kako bi se sintetizirali, definirali i organizirali elementi tih slojeva. Postojeći pokazatelji i indeksi urbane mobilnosti uglavnom su ciljano The authors have upgraded the index by the inclusion of the route relevance factor in the model, as presented in Equation 5 [15]:…”

Section: Tti Free Flow Travel Timeunclassified

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An Overview of Indicators and Indices Used for Urban Mobility Assessment

Vidović

Šoštarić

Budimir

2019

PROMET

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The urban mobility is affected by global trends resulting in a growing passenger and freight transport demand. In order to improve the understanding of urban mobility in general, to evaluate mobility services and to quantify the overall transport system performance, it is necessary to assess urban mobility. Urban mobility assessment requires the application of methodology integrating different metrics and explicitly applying a multi-dimensional approach. Since scientific community does not define urban mobility in an unambiguous way, part of this paper is devoted to the analysis of the definition of urban mobility. This step enables better understanding of urban mobility in general, as well as understanding of the urban mobility assessment process. Usually, a three-layered approach that includes urban mobility data, indicators and indices is used for the assessment. Therefore, the aim of this paper was to perform extensive research in order to synthesize, define and organize the elements of those layers. The existing urban mobility indicators and indices have been developed for specific urban areas, taking into account local specifications, and they are not applicable in other cities. Also, the choice of urban mobility indicators is mainly related to the existence of data sources, which limits the objective and comparable assessment of the mobility of cities where such data do not exist.

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Section: Related Data Sources and Datamentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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An Overview of Indicators and Indices Used for Urban Mobility Assessment

Vidović

Šoštarić

Budimir

2019

PROMET

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“…Making transport and mobility more intelligent has been a long-term goal since the 60s (e.g., Electronic Route Guidance System developed by the US Federal Highway Association) and 70s (e.g., the Japanese Comprehensive Automobile Traffic Control System) [1] and through the implementation of cooperative intelligent transport systems (C-ITS) [2], [3]. According to ERTICO 1 , intelligent mobility offers the opportunity for "fully informed people, zero accidents, zero delays, with reduced impact on the environment, where services are affordable and seamless, with privacy respected and security provided".…”

Section: Introductionmentioning

confidence: 99%

CoDRIVE – Delivering High Accuracy, Ubiquitous Positioning Through Combined Radio Navigation and Inertial Sensing Technologies

Roberts¹,

Meng²,

Xu³

et al. 2020

Preprint

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The CoDRIVE solution builds on R&amp;D in the development of connected and autonomous vehicles (CAVs). The mainstay of the system is a low-cost GNSS receiver integrated with a MEMS grade IMU powered with CoDRIVE algorithms and high precision data processing software. The solution integrates RFID (radio-frequency identification) localisation information derived from tags installed in the roads around the University of Nottingham. This aids the positioning solution by correcting the long-term drift of inertial navigation technology in the absence of GNSS. The solution is informed of obscuration of GNSS through city models of skyview and elevation masks derived from 360-degree photography. The results show that predictive intelligence of the denial of GNSS and RFID aiding realises significant benefits compared to the inertial only solution. According to the validation, inertial only solutions drift over time, with an overall RMS accuracy over a 300 metres section of GNSS outage of 10 to 20 metres. After deploying the RFID tags on the road, experiments show that the RFID aided algorithm is able to constrain the maximum error to within 3.76 metres, and with 93.9% of points constrained to 2 metres accuracy overall.

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“…In recent research work (FP7 CONDUITS) such an evaluation framework was formulated, consisting of a set of Key Performance Indicators (KPIs) for four themes of mobility management: efficiency, safety, pollution reduction and social inclusion [2] . The KPIs were subsequently validated through before-and afterevaluation of real-world case studies in the cities of Paris, Rome, Tel Aviv and Munich, using real data supplied by the local authorities and transport operators [3][4] . Through the conduct of the case studies, it was concluded that the KPIs were easy to apply and required already available data, thus forming a very useful evaluation tool for assisting city decision makers of in the field of mobility management and ITS, and to some extent for identifying best practice and lessons learnt elsewhere.…”

Section: Introductionmentioning

confidence: 99%

Policy-aware assessment of environmental impacts from transport in smart cities

et al. 2017

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Abstract:In recent research a performance evaluation framework for traffic management and Intelligent Transport Systems was developed, consisting of a set of Key Performance Indicators (KPIs) for the themes of traffic efficiency, safety, pollution reduction and social inclusion, all of which are key components of a smart city. One of the innovative elements of these KPIs is their ability to consider the transport policy layer, in the sense that the evaluation of the suitability and effectiveness of different strategies and ITS options is calculated in relation to the decision maker's high-level transport policy rather than objectively. This is achieved through weighting factors, whereby more important policy objectives are weighted more heavily in the calculation. But while the theoretical framework is ready to accommodate the policy layer, no methodology to determine the values of the weighting factors has been developed so far. The present study, therefore, concentrates on the development and testing of such a methodology, focusing on the environmental impact aspect of urban mobility management and ITS in the context of smart cities. The development is based on existing policy objectives and legislation in different cities and countries, while testing is carried out using the purposedeveloped CONDUITS_DST software with data from microsimulation models before and after the implementation of a bus priority signalling system in Brussels, Belgium. The results show that the method captures the expected effects, but also that it is able to reflect policy objectives and deliver evaluation results in relation to their alignment with those.

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Development and Application of an Evaluation Framework for Urban Traffic Management and Intelligent Transport Systems

Cited by 19 publications

References 4 publications

An Overview of Indicators and Indices Used for Urban Mobility Assessment

An Overview of Indicators and Indices Used for Urban Mobility Assessment

CoDRIVE – Delivering High Accuracy, Ubiquitous Positioning Through Combined Radio Navigation and Inertial Sensing Technologies

Policy-aware assessment of environmental impacts from transport in smart cities

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