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Congestion and Safety: A Spatial Analysis of London


  • Robert B. Noland


  • Mohammed A. Quddus



Spatially disaggregate Enumeration District (ED) level data for London is used in an analysis of various area-wide factors on road casualties. Data on 15335 EDs was input into a geographic information system (GIS) that contained data on road characteristics, public transport accessibility, information of nearest hospital location, car ownership and road casualties. Demographic data for each ED was also included. Various count data models e.g., negative binomial or zero-inflated Poisson and negative binomial models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Different levels of spatial aggregation were also examined to determine if this affected interpretation of the results. Different pedestrian casualties were also examined. Results suggest that dissimilar count models may have to be adopted for modeling different types of accidents based on the dependent variable. Results also suggest that EDs with more roundabouts are safer than EDs with more junctions. More motorways are found to be related to fewer pedestrian casualties but higher traffic casualties. Number of households with no car seems to have more traffic casualties. Distance of the nearest hospital from EDs tends to have no significant effect on casualties. In all cases, it is found that EDs with more employees are associated with fewer casualties.

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  • Robert B. Noland & Mohammed A. Quddus, 2003. "Congestion and Safety: A Spatial Analysis of London," ERSA conference papers ersa03p66, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa03p66

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    1. Dickerson, Andrew & Peirson, John & Vickerman, Roger, 2000. "Road Accidents and Traffic Flows: An Econometric Investigation," Economica, London School of Economics and Political Science, vol. 67(265), pages 101-121, February.
    2. Daniel Shefer & Piet Rietveld, 1997. "Congestion and Safety on Highways: Towards an Analytical Model," Urban Studies, Urban Studies Journal Limited, vol. 34(4), pages 679-692, April.
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    Cited by:

    1. Stefan Bauernschuster & Timo Hener & Helmut Rainer, 2017. "When Labor Disputes Bring Cities to a Standstill: The Impact of Public Transit Strikes on Traffic, Accidents, Air Pollution, and Health," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 1-37, February.
    2. Holguín-Veras, José, 2011. "Urban delivery industry response to cordon pricing, time-distance pricing, and carrier-receiver policies in competitive markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 802-824, October.
    3. Fosgerau, Mogens & Lindsey, Robin, 2013. "Trip-timing decisions with traffic incidents," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 764-782.
    4. Holgui­n-Veras, Jose & Cetin, Mecit & Xia, Shuwen, 2006. "A comparative analysis of US toll policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(10), pages 852-871, December.
    5. Peck, Dana & Scott Matthews, H. & Fischbeck, Paul & Hendrickson, Chris T., 2015. "Failure rates and data driven policies for vehicle safety inspections in Pennsylvania," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 252-265.
    6. Ilias-Nikiforos Pasidis, 2015. "Congestion by accident? Traffic and accidents in England," ERSA conference papers ersa15p1321, European Regional Science Association.
    7. Robert Noland & Mohammed Quddus & Washington Ochieng, 2008. "The effect of the London congestion charge on road casualties: an intervention analysis," Transportation, Springer, vol. 35(1), pages 73-91, January.
    8. Holgun-Veras, Jos & Cetin, Mecit, 2009. "Optimal tolls for multi-class traffic: Analytical formulations and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 445-467, May.

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