Safety models incorporating graph theory based transit indicators

Quintero, Liliana; Sayed, Tarek; Wahba, Mohammed

doi:10.1016/j.aap.2012.06.012

Cited by 30 publications

(9 citation statements)

References 14 publications

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“…As for organization factors, unlike Quintero et al (2013) but according to Goh et al (2014) these results show that the right of way priority strategy will reduce the associated values of frequency and the severity if the exposure value is up to approximately 4*10 6 passenger*km.…”

Section: Resultsmentioning

confidence: 92%

“…The Generalized Linear Modelling approach that assumes a negative binomial structure seems to be the best way to predict the frequency of crashes because road crashes are random, infrequent and non-negative integer events (e.g., Mannering and Bath, 2014). Nevertheless, a non-linear relationship exists between the frequency of crashes and traffic exposure factors (e.g., Cheung et al, 2008;Quintero et al, 2013). Moreover, exposure Ei,l refers to a variable that when it assumes zero value, the frequency of crashes and severities must be zero.…”

Section: Methods For Risk Assessmentmentioning

confidence: 99%

“…Perhaps, it is generally accepted that bus transit improves road safety since it is expected to reduce vehicular traffic (e.g., Albertsson and Falkmer, 2005;Cafisio et al, 2013). Bus safety research mainly deals with studies on: i) descriptive statistics on the occurrences of bus crashes and associate severities (e.g., Evans and Courtney, 1985;Jovanis et al, 1991;Zeeger et al, 1994) and ii) multivariable prediction models that shed light on the effects of different safety factors (determinants, or attributes, or variables) on the frequency (e.g., Cheung et al, 2008;Strathman et al, 2010;Quintero et al, 2013;Goh et al, 2014) and the severity of the bus crashes (e.g., Kaplan and Prato, 2012;Prato and Kaplan, 2014). In addition, the literature is quite rich on risk assessment methods in several fields such as chemical, economic and financial, engineering, industrial, medical (e.g., Fine, 1971;CCPS, 1995;Andrews andMoss (2002) ISO 39001, 2009;Mullai, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Risk of Bus Crashes in Transit Systems

Porcu¹

2021

European Transport/Trasporti Europei

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Although public transport buses may be considered a safe transportation mode, bus safety is a crucial issue from the perspectives of operators, passengers and local authorities owing to the relevant implications it generates. Therefore, assessing the risk of crashes on bus routes may help improve the safety performance of transit operators. Much research has identified patterns of bus crashes to understand the effects of many factors on the frequency and the severity of them. Conversely, to the best of our knowledge, the research measuring the risk of crashes in bus transit networks is seldom faced. This paper adjusts existing methods to assess the safety on bus transit networks by the integration of safety factors, prediction models and risk methods. More precisely, first, the methodology identifies several safety factors as well as the exposure risk factors. Second, this methodology specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes and models their relationships in a risk function. Third, this methodology computes the risk of crashes for each route and provides a ranking of safety performance. A real case study demonstrates the feasibility of this methodology using 3,457 bus crashes provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology could be implemented in a road traffic safety management system in order to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms.

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Section: Resultsmentioning

confidence: 92%

Section: Methods For Risk Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Risk of Bus Crashes in Transit Systems

Porcu¹

2021

European Transport/Trasporti Europei

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“…Disaggregate analyses at the street level (e.g., intersections or roads) are necessary to estimate the effect of specific roadway characteristics on the likelihood of injury and death. Several Canadian studies developed collision prediction models to predict transit collisions at the zonal, intersection, and arterial levels accounting for transit network attributes as well as some geometry characteristics [ 17 – 19 ]. These studies however were limited to transit/bus crashes, and they did not compare the safety of car versus bus travel.…”

Section: Introductionmentioning

confidence: 99%

Traveling by Bus Instead of Car on Urban Major Roads: Safety Benefits for Vehicle Occupants, Pedestrians, and Cyclists

et al. 2018

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Some studies have estimated fatality and injury rates for bus occupants, but data was aggregated at the country level and made no distinction between bus types. Also, injured pedestrians and cyclists, as a result of bus travel, were overlooked. We compared injury rates for car and city bus occupants on specific urban major roads, as well as the cyclist and pedestrian injuries associated with car and bus travel. We selected ten bus routes along major urban arterials (in Montreal, Canada). Passenger-kilometers traveled were estimated from vehicle counts at intersections (2002–2010) and from bus passenger counts (2008). Police accident reports (2001–2010) provided injury data for all modes. Injury rates associated with car and bus travel were calculated for vehicle occupants, pedestrians, and cyclists. Injury rate ratios were also computed. The safety benefits of bus travel, defined as the number of vehicle occupant, cyclist, and pedestrian injuries saved, were estimated for each route. Overall, for all ten routes, the ratio between car and bus occupant injury rates is 3.7 (95% CI [3.4, 4.0]). The rates of pedestrian and cyclist injuries per hundred million passenger-kilometers are also significantly greater for car travel than that for bus travel: 4.1 (95% CI [3.5, 4.9]) times greater for pedestrian injuries; 5.3 (95% CI [3.8, 7.6]) times greater for cyclist injuries. Similar results were observed for fatally and severely injured vehicle occupants, cyclists, and pedestrians. At the route level, the safety benefits of bus travel increase with the difference in injury rate associated with car and bus travel but also with the amount of passenger-kilometers by bus. Results show that city bus is a safer mode than car, for vehicle occupants but also for cyclists and pedestrians traveling along these bus routes. The safety benefits of bus travel greatly vary across urban routes; this spatial variation is most likely linked to environmental factors. Understanding the safety benefits of public transit for specific transport routes is likely to provide valuable information for mobilizing city and transportation planners.

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“…Quintero-Cano et al . [ 7 ] focused on pre-existing network indicators, whereas Kansky summarized the definitions of transit network properties [ 3 ]. In addition, Quintero-Cano [ 8 ] presented several macro-level prediction models for transit infrastructure, transportation network topology, transit route design, and transit performance and operations.…”

Section: Introductionmentioning

confidence: 99%

Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network

et al. 2015

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Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality’s closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network’s growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

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Safety models incorporating graph theory based transit indicators

Cited by 30 publications

References 14 publications

Assessing the Risk of Bus Crashes in Transit Systems

Assessing the Risk of Bus Crashes in Transit Systems

Traveling by Bus Instead of Car on Urban Major Roads: Safety Benefits for Vehicle Occupants, Pedestrians, and Cyclists

Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network

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