gis multicriteria pedestrian crossing risk assessment in bus rapid transit

7/30/2019 GIS multicriteria pedestrian crossing risk assessment in bus rapid transit

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1 INTRODUCTIONTransoeste is a BRT corridor in the West Zone ofRio de Janeiro. It connects the region of Barra da Ti-

juca to Santa Cruz, being completely apart from thegeneral traffic in its majority. Nowadays it takes less

than one hour to travel on a route that has usuallytaken one hour and forty minutes (SILVA DE SOU-ZA, 2012).

The implantation of a BRT corridor is a big engi-neering job that involves a large change in the trafficroutine. Thus, there were several mishaps duringTransoestes implantation. Since its operation began,in June of 2012, there were five accidents with fatalvictims registered. In all these cases, the pedestrianswere crossing the street inappropriately.

In fact, this characteristic is not exclusive ofTransoestes system. TIWARI & JAIN (2012) ana-lyzed New Delhi BRT and showed that, after its in-auguration in 2008, the pedestrians turned out to bethe most vulnerable category in terms of accidents.Along the 6 km corridor there were four fatal acci-dents during the eight fist months of operation. Thisscenery could only be changed through engineeringfits that minimized the risks.

Other examples can be found in the internationalliterature. For example, RODRGUEZ & BRISSON(2009) distinguish the influence of connectivity andmicroaccessibility on pedestrian flows near the sta-

tions of Transmilenio, in Bogot.In this work we have analyzed the 13 km stretchon Avenida das Amricas between Terminal Al-vorada and Recreio Shopping. It was considered the

most critical section of Transoestes corridor interms of running overs.

In order to establish which areas on this stretchare the most critical, two methodologies were cho-sen. The first was to go on a site visit to evaluate thecrossings near each bus stop along the section, col-

lecting data for an Analytic Hierarchical Procedurematrix. The second one included a simple spatial re-gression of the historical data about running oversfor the model analysis.

2 ON SITE SURVEYA site visit was performed in July 2012 in order todiagnose the pedestrian crossing conditions alongthe evaluated section on Transoeste. The most inap-

propriate crossings occurred when pedestrians wereboarding or alighting the conventional buses, in sev-eral stops along the side lane. Thus we can assumethat the accessibility to the BRT stations is a majoraspect, but it should not be considered as the mainfocus of the evaluation in this case study.

ZEEGER & OPIELA (1985) studied 1,297 runovers in 15 American cities. They found out that, infact, the accident prediction model they obtained in-dicates there were more accidents on the sites nearer

bus stops. It happened especially due to the visualobstruction caused by the buses and the bus stop

equipment, for both pedestrians and drivers.Based on this assumption we evaluated 43 busstops, in both directions, located in the influencezone of the first 18 stations on the corridor. Geomet-ric features and the several kinds of inappropriate

GIS multicriteria pedestrian crossing risk assessment in bus rapid transit

P. P. Silva de SouzaUniversidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

I. C. Leal JuniorUniversidade Federal Fluminense, Rio de Janeiro, Brazil

ABSTRACT: Pedestrian crashes in the surroundings of transit stations are linked not only with immediate ac-cess conditions. A wide range of land use and micro-accessibility indicators highly influence pedestrian risk,even for non-commuters. In this paper, pedestrian risk was evaluated in Transoeste Bus Rapid Transit, in Riode Janeiro, through a multi-criteria decision matrix and further spatial data analysis. A newly implemented

system, the segregated fast lanes of this BRT corridor caused five pedestrian casualties in six months of oper-ation. The objective of this study is to provide operators with a risk heatmap indicating which areas requiresspecial attention, such as operational interventions or educational campaigns.


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pedestrian crossings were measured. Figures 1 and 2show some of the most common project misconcep-tions encountered during the visit.

Figure 1 Pedestrians crossing inappropriately, encouraged bythe long walking distance up to the zebra crossing.

Figure 2 Bus bay incorrectly placed, reducing the sidewalk toa minimum extent.

3 ANALYTIC HIERARCHY PROCESSOne of the most used methods for multicriteria deci-sions all over the world is the Analytic HierarchyProcess (AHP). It was created by Thomas. L. Saatyin the mid-1970. It emerged to promote the over-coming of cognitive limits of the managers who takedecisions. According to this method, the problem oftaking decision can be usually broken down in hier-archic levels, making it easier to understand andevaluate the issues (BARAAS & MACHADO,2006). Thus, AHP poses itself as a optimal toolwhen dealing with problems that involve a plurality

of abstract factors, as those encountered in the pe-destrian risk assessment.

In order to prepare the AHP decision matrix theevaluations made on the site visit were traduced asindexes. This matrix will point the risks of crossingaround each station.

3.1Indexes DefinitionMost studies indicate that the volumes of pedestrianand traffic are major determinant issues for the run-ning over frequency. They also point the vehiclespeed as one of the principal factors that contributefor the severity of these accidents (MORENO &MORENCY, 2011).

However, two observations should be made forthe situation studied in this work. First, the pedestri-an volume data of the evaluated region are weaklyavailable. Besides, the main focus of this work is toinfer some steps that could be adopted to reduce the

risks of accidents. So the variables related to pedes-trian and vehicle volumes would be inefficient asthey could hardly be moderated.

A fewer number of studies measured the effectsof the geometric graphic attributes, such as the roadwidth, the number of lanes, the occurrence of cross-ings, the existence of median strip, the sort of turnrestrictions, etc. (HARWOOD & TORBIC, 2008).

In the BRT Transoeste study we adopted fivemain indexes: Access to BRT station: it is a Boolean variable. It

assumes whether there are or not BRT feeder buslines on the observed stop. Total walking distance: it represents the passen-

ger impedance for crossing the street on appropri-ate locals. The larger the walking distance the

bigger the impedance.Negative displacement extension: it is considered

one of the most critical variables. It represents thepossibility for the passenger of performing an in-correct crossing when the BRT station and theordinary bus stop are aligned (Figure 3).

Pedestrian flow: it is a qualitative variable of thepedestrian volumes in the region, based on thesurrounding hubs such as shopping centers andcondominiums.

Sidewalk quality: it is a qualitative variable aboutthe sidewalk quality in the region. It shuttles theimpedance of the passenger walking distance to-wards the appropriate crossing place, since thereare areas with no sidewalk.

After defining the indexes, the distance values weremeasured in proper geoprocessing software, while

the semantic observations were converted to numer-ical values and tabulated, as shown on Table 1.


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Figure 3 Most common situation when a negative displace-ment ocurrs.

3.2 Creating the AHP model using the softwareExpert Choice

The model was developed in the commercial soft-ware Expert Choice, that facilitates the develop of

AHP analysis using standardized indexes in a 0-1scale. We obtained the weight among indexes bycomparing pairwise. This is one of the major ad-vantages of the AHP compared to the Delphi method(AL-HARBI, 2001).

For each of the numeric values, the standardiza-tion was obtained adapting the linear equation forthe variables of Total Walking Distance and Nega-tive Displacement Extension.

Following, indexes were compared pairwisebased on the opinion of the experts that had gone to

the site visit. The ones that got the higher weight cri-teria were those which produce more run overs, as itfollows below:Negative displacement (0,48) Pedestrian flow (0,24) Walking distance (0,20) Sidewalk quality (0,04) BRT station access (0,04)

Finally, all the values shown on Table 1 whereadded to the software in order to run the analysis.Thus we got the summary presented on Figure 4, or-dered from higher to lower risks of running overs on

the BRT corridor.

Table 1. Pedestrian risk assessment indexes obtainedduring site visit, for each regular bus stop________________________________________________Bus BRT Stop-Z* Z-Station* People SidewalkStop Acess Distance. Distance Flow Quality________________

m m________________________________________________1A YES 150 -35 MED POOR1B YES 300 -35 MED POOR2A YES UNDERGROUND PASS

2B NO 280 HIGH GOOD2C NO 230 HIGH GOOD3A YES 90 35 HIGH POOR4A NO 390 MED POOR4B NO 400 MED POOR4C YES 395 35 LOW POOR4D YES 315 35 LOW POOR5A YES 55 -35 LOW GOOD6A NO 530 LOW POOR6B NO 320 LOW POOR7A YES 100 -35 LOW POOR7B NO 320 LOW POOR8A YES 50 -30 MED GOOD9A YES 30 -35 LOW POOR

10A YES 80 80 HIGH GOOD10B YES 80 -80 HIGH GOOD11A YES 75 -40 MED GOOD11B YES 60 40 MED GOOD12A YES 35 35 HIGH GOOD12B YES 12 35 HIGH GOOD12C YES 35 -35 HIGH GOOD13A YES 50 -35 MED GOOD13B YES 30 -35 MED GOOD13C NO 280 MED GOOD13D NO 90 MED GOOD14A NO 85 LOW GOOD14B YES 50 -35 MED GOOD

14C YES 65 35 MED GOOD15A YES 45 -35 LOW GOOD15B YES 130 35 MED GOOD15C NO 210 MED GOOD15D NO 210 MED GOOD16A YES 110 -35 HIGH GOOD16B YES 0 35 HIGH GOOD17B YES 72 35 LOW GOOD17C YES 60 -35 LOW GOOD17D NO 135 LOW GOOD17E NO 135 LOW GOOD18A YES 170 -70 HIGH GOOD18B YES 30 -70 HIGH GOOD_________________________________________________

* Distances between the zebra crossing and the bus stops andthe BRT station, respectively. Negative value indicates nega-tive displacement.

4 SPATIAL ANALYSIS OF AHP RESULTSFor a better understanding of the obtained results, aspatial analysis was run on the pedestrian risk, in or-der to make a comparison with historical data avail-able on the region prior to the BRT implementation.


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Figure 5 Comparison between AHP spatial results and historical data, respectively.

4.1 Kernel density estimationThe Kernel density estimation indicates how often a

punctual process happens throughout the study re-gion, associating events intensity per area unity to abidimensional surface. (ROCHA & NASSI, 2012).

The spatial regression of the AHP matrix shownsimilar results to the kernel density through histori-cal accidents data, acquired from the police databasefrom 2008 to 2011. The most critical spot observedon historical data is exactly the location of Bosqueda Barra station, and where 60% of the deaths havehappened so far.

Thus, one can infer that, while the model hasshown a great similarity to actual data, no special at-tention was paid to this situation prior to the imple-mentation of the BRT Corridor.

Figure 4 AHP matrix top 20 results, ordered from highest tolowest pedestrian risk

5 CONCLUSIONSThis preliminary work has provided a guideline of

which stretches were more likely to accidents and,then, deserved special attention. That way, the sys-tem operator was able to focus on these placesthrough education campains.Even though no complex intervention has beenmade, like the unalignament of bus stops, for exam-

ple, no other pedestrian crash has been recordedsince the application of this work.The future potential is to obtain a wider model, in away that is possible to predict the critical hotspotsduring the project phase, even before the implemen-tation of the BRT corridor.

REFERENCES

AL-HARBI, K.-S. (2001). Application of the AHP in projectmanagement. International Journal of Project Management19, pp. 19-27.

BARAAS, F., & MACHADO, J. (2006). A anlise multicrit-rio na tomada de deciso o Mtodo Analtico Hierrquicode T. L. Saaty. Princpios fundamentais e seu desenvolvi-mento. Instituto Politcnico de Coimbra. Departamento deEngenharia Civil.

HARWOOD, D., & TORBIC, D. (2008). Pedestrian safetyprediction methodology. Final Report. Washington D.C.:NCHRP (National Cooperative Highway Research Pro-gram).

MORENO, L., & MORENCY, P. (2011). The link betweenbuilt environment, pedestrian activity and pedestrianvehicle collision occurrence at signalized intersections. Ac-cident Analysis and Prevention 43.

ROCHA, M., & NASSI, C. (2012). Anlise Estatstica e daDistribuio Espacial dos Acidentes de Trnsito na ZonaSul da Cidade do Rio de Janeiro . PLURIS.

RODRGUEZ, D., & BRISSON, E. (2009). The relationshipbetween segment-level built environment attributes and pe-destrian activity around Bogotas BRT stations. Transporta-

tion Research Part D 14, pp. 470-478.SILVA DE SOUZA, P. (2012). Evoluo da Rede de Mobili-dade Metropolitana do Rio de Janeiro. Escola Politcnicada Universidade Federal do Rio de Janeiro. Departamentode Engenharia de Transportes.


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TIWARI, G., & JAIN, D. (2012). Accessibility and safety indi-cators for all road users: case study Delhi BRT. Journal ofTransport Geography, pp. 87-95.

ZEGEER, C., & OPIELA, K. (1985). Pedestrian SignalizationAlternatives. Federal Highway Administration.

gis multicriteria pedestrian crossing risk assessment in bus rapid transit

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