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Predicting the effect of various ISA penetration grades on pedestrian safety by simulation
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
2005 (English)In: Accident Analysis and Prevention, ISSN 0001-4575, E-ISSN 1879-2057, Vol. 37, no 6, 1162-1169 p.Article in journal (Refereed) Published
Abstract [en]

Intelligent speed adaption (ISA) is one type of vehicle-based intelligent transportation systems (ITS), which warns and regulates driving speed according to the speed limits of the roads. Early field studies showed that ISA could reduce general mean speed levels and their variances in different road environments. This paper studies the effects of various ISA penetration grades on pedestrian safety in a single lane road. A microscopic traffic simulation tool, TPMA, was further developed and used to implement different ISA penetration grades. Momentary spot speed and traffic flow data are first logged in the traffic simulation for later prediction of pedestrian safety. Then a hypothetical vehicle-pedestrian collision model is extended from early researches in order to estimate two safety indicators: probability of collision, and risk of death. Finally, Monte Carlo method is applied iteratively to compute those safety indices. The computational result shows that raising ISA penetration in traffic flow will reduce both the probability of mid-block collision between vehicle and pedestrian and the risk of death in the collision accidents. Furthermore, the decrease of the risk of death will be more prominent than that of the collision probability according to this method.

Place, publisher, year, edition, pages
2005. Vol. 37, no 6, 1162-1169 p.
Keyword [en]
ISA penetration; pedestrian safety; collision model; TPMA simulation; Monte Carlo experiment
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-6696DOI: 10.1016/j.aap.2005.06.017ISI: 000233183200022Scopus ID: 2-s2.0-26944492180OAI: oai:DiVA.org:kth-6696DiVA: diva2:11476
Note
QC 20100602Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2011-11-10Bibliographically approved
In thesis
1. Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
Open this publication in new window or tab >>Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis is concerned with modeling of driver behavior based on data collected from real traffic using an advanced instrumented vehicle. In particular, the focus is on driver-following behavior (often called car-following in transport science) for microscopic simulation of road traffic systems. In addition, the modeling methodology developed can be applied for the design of human-centered control algorithms in adaptive cruise control (ACC) and other longitudinal active-safety technologies.

Driver behavior is a constant research topic in the modeling of traffic systems and Intelligent Transportation Systems (ITS), which could be traced back to the work of GeneralMotor (GM) Co. in 1950’s. In the early time, researchers were only interested in the development of driver models fulfilling basic physical properties and producing reasonable flow dynamics on a macroscopic level. With the booming interest on driver modeling on a microscopic level and needs in ITS developments, researchers now emphasize modeling using microscopic data acquired from real world. To follow this research trend, a methodological framework on car-following data acquisition, analysis and modeling has been developed step by step in this thesis, and the basic idea is to build a computational model for car-following behavior by exploration of collected data. To carry out the work, different techniques within the field of modern Artificial Intelligence (AI), namely Computational Intelligence (CI)1, have been applied in the research subtasks e.g. information estimation, behavioral regime classification, regime model integration and model estimation. Therefore, a preliminary introduction of the CI methods being used in this thesis work is included in the text.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. 81 p.
Series
Trita-TEC-PHD, ISSN 1653-4468 ; 06:004
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-4253 (URN)978-91-85539-11-6 (ISBN)91-85539-11-2 (ISBN)
Public defence
2007-01-19, Sal F3, KTH, Lindstedtsvägen 26, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC 20100602Available from: 2006-12-29 Created: 2006-12-29 Last updated: 2010-06-02Bibliographically approved

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