Investigating Different Road Safety Implications of Two TDM Policy Measures: Fuel-Cost Increase and Teleworking

Travel demand management (TDM) consists of a variety of policy measures that affect the transportation system’s effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios; a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models (CPMs) that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear modeling (GWGLM) framework are developed to incorporate the spatial variations in association between the number of crashes (NOCs) (including fatal, severe and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network and socio-demographic variables of 2200 traffic analysis zones (TAZs) in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this study, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

[1]  Ali Naderan,et al.  Aggregate crash prediction models: introducing crash generation concept. , 2010, Accident; analysis and prevention.

[2]  Jutaek Oh,et al.  Forecasting Crashes at the Planning Level: Simultaneous Negative Binomial Crash Model Applied in Tucson, Arizona , 2004 .

[3]  Tarek Sayed,et al.  Macro-level collision prediction models for evaluating neighbourhood traffic safety , 2006 .

[4]  Michael A. Morrisey,et al.  Gasoline prices and motor vehicle fatalities , 2004 .

[5]  N. Levine,et al.  Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators. , 1995, Accident; analysis and prevention.

[6]  Geert Wets,et al.  Application of Different Exposure Measures in Development of Planning-Level Zonal Crash Prediction Models , 2012 .

[7]  P. Jovanis,et al.  Spatial analysis of fatal and injury crashes in Pennsylvania. , 2006, Accident; analysis and prevention.

[8]  J M Nilles WHAT DOES TELEWORK REALLY DO TO US , 1996 .

[9]  Dennis K. Henderson,et al.  Impacts of Center-Based Telecommuting on Travel and Emissions: Analysis of the Puget Sound Demonstration Project , 1996 .

[10]  Tse-Chuan Yang,et al.  SAS macro programs for geographically weighted generalized linear modeling with spatial point data: Applications to health research , 2012, Comput. Methods Programs Biomed..

[11]  Tarek Sayed,et al.  Macrolevel Collision Prediction Models to Enhance Traditional Reactive Road Safety Improvement Programs , 2007 .

[12]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[13]  P. Goodwin,et al.  Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income: A Review , 2004 .

[14]  Patricia L. Mokhtarian,et al.  The trade-off between trips and distance traveled in analyzing the emissions impacts of center-based telecommuting , 1998 .

[15]  M. Quddus,et al.  A spatially disaggregate analysis of road casualties in England. , 2004, Accident; analysis and prevention.

[16]  Todd Litman,et al.  THE ONLINE TDM ENCYCLOPEDIA: MOBILITY MANAGEMENT INFORMATION GATEWAY , 2003 .

[17]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[18]  Todd Litman Changing Vehicle Travel Price Sensitivities , 2011 .

[19]  Rajiv Bhatia,et al.  An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. , 2009, Accident; analysis and prevention.

[20]  Todd Litman Changing Vehicle Travel Price Sensitivities: The Rebounding Rebound Effect , 2011 .

[21]  I Van Schagen,et al.  The uses of exposure and risk in road safety studies , 2002 .

[22]  Meiwu An,et al.  Using Travel Demand Model and Zonal Safety Planning Model for Safety Benefit Estimation in Project Evaluation , 2011 .

[23]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[24]  Mohamed Abdel-Aty,et al.  Managing Roadway Safety at the Traffic Analysis Zones Level , 2011 .

[25]  J L Martin,et al.  Comparison of road crashes incidence and severity between some French counties. , 2003, Accident; analysis and prevention.

[26]  J. Hintze,et al.  Violin plots : A box plot-density trace synergism , 1998 .

[27]  Joel Freedman,et al.  Synthesis of first practices and operational research approaches in activity-based travel demand modeling , 2007 .

[28]  Davy Janssens,et al.  An estimation of total vehicle travel reduction in the case of telecommuting. Detailed analyses using an activity-based modeling approach. , 2011 .

[29]  Alireza Hadayeghi,et al.  Temporal transferability and updating of zonal level accident prediction models. , 2006, Accident; analysis and prevention.

[30]  Xuesong Wang,et al.  Modeling signalized intersection safety with corridor-level spatial correlations. , 2010, Accident; analysis and prevention.

[31]  A. Shalaby,et al.  Macrolevel Accident Prediction Models for Evaluating Safety of Urban Transportation Systems , 2003 .

[32]  Paul P Jovanis,et al.  Analysis of Road Crash Frequency with Spatial Models , 2008 .

[33]  Peter Vovsha,et al.  Advanced activity-based models in context of planning decisions , 2006 .

[34]  Takayuki Morikawa,et al.  Impact assessment of satellite centre-based telecommuting on travel and air quality in developing countries by exploring the link between travel behaviour and urban form , 2008 .

[35]  Mohamed Abdel-Aty,et al.  Zonal-Level Safety Evaluation Incorporating Trip Generation Effects , 2011 .

[36]  Gordon Richard Lovegrove,et al.  Community-based, macro-level collision prediction models , 2006 .

[37]  Tom Brijs,et al.  Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling. , 2013, Accident; analysis and prevention.

[38]  Chao Wang,et al.  Impact of traffic congestion on road accidents: a spatial analysis of the M25 motorway in England. , 2009, Accident; analysis and prevention.

[39]  David W. S. Wong,et al.  Statistical Analysis with ArcView GIS , 2000 .

[40]  Geert Wets,et al.  Developing Zonal Crash Prediction Models with a Focus on Application of Different Exposure Measures , 2012 .

[41]  Robert B Noland,et al.  The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois county-level data. , 2004, Accident; analysis and prevention.

[42]  Bhagwant Persaud,et al.  Safety Prediction Models , 2007 .

[43]  Davy Janssens,et al.  Modelling Short-Term Dynamics in Activity-Travel Patterns: Conceptual Framework of the Feathers Model , 2007 .

[44]  Mohamed Abdel-Aty,et al.  Temporal and spatial analyses of rear-end crashes at signalized intersections. , 2006, Accident; analysis and prevention.

[45]  Andrew P Tarko,et al.  Tool with Road-Level Crash Prediction for Transportation Safety Planning , 2008 .

[46]  Guangqing Chi,et al.  Gasoline Prices and Traffic Safety in Mississippi , 2010, Journal of safety research.

[47]  Helai Huang,et al.  County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling , 2010 .

[48]  Brett E. Koenig,et al.  The Travel and Emissions Impacts of Telecommuting for the State of California Telecommuting Pilot Project , 1995 .

[49]  Benoît Flahaut,et al.  Impact of infrastructure and local environment on road unsafety. Logistic modeling with spatial autocorrelation. , 2004, Accident; analysis and prevention.

[50]  Bhagwant Persaud,et al.  Development of Planning-Level Transportation Safety Models using Full Bayesian Semiparametric Additive Techniques , 2010 .

[51]  P. Mokhtarian,et al.  Telecommunications and travel demand and supply: Aggregate structural equation models for the US , 2007 .

[52]  Todd Alexander Litman,et al.  Macrolevel Collision Prediction Models to Evaluate Road Safety Effects of Mobility Management Strategies: New Empirical Tools to Promote Sustainable Development , 2008 .

[53]  Mohamed Abdel-Aty,et al.  Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.

[54]  M. Quddus Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data. , 2008, Accident; analysis and prevention.

[55]  Bani K. Mallick,et al.  ROADWAY TRAFFIC CRASH MAPPING: A SPACE-TIME MODELING APPROACH , 2003 .

[56]  Todd Litman,et al.  Safe Travels: Evaluating Mobility Management Traffic Safety Impacts , 2009 .

[57]  Todd Litman Mobility Management Traffic Safety Impacts , 2006 .

[58]  P. Mokhtarian,et al.  Does telecommuting reduce vehicle-miles traveled? An aggregate time series analysis for the U.S. , 2005 .