Volume 3 - Issue 1 - 2017


Single-Vehicle Crashes on Rural Two-Lane Highways and Injury Severity: Does the Age Matter?

Mahdi Pour-Rouholamin, Ph.D., Huaguo Zhou, Ph.D., PE

Abstract
Single-vehicle crashes on rural two-lane highways impose a considerable risk to road users due to their higher severity outcome compared to other crashes on these facilities. Furthermore, considerable variation in the severity among various age groups (young, middle-aged, and older drivers) has been noticed, corroborating the need for analyzing age-classified data. Crash data from Alabama was compiled and classified based on the age group. For each age class, a generalized ordered logit model was developed to identify the effect of various variables on injury severity. This model can consider ordered nature of severity as well as provide flexibility in calculating the parameter estimates. Driver gender, seatbelt use, damage to the vehicle, driving on county roads, hitting a fixed object and animal, and speeding were found to be significant in all developed models. Intoxication is a significant factor that affects injury severity for young drivers. Time of day also significantly affects the injury severity for older drivers. Vehicle age and driving with invalid license were not found to affect injury severity for older drivers, while they affected the other age groups. It was shown that some factors have significant effect on the injury severity for all age groups while others have varying effect across different age groups. The results of this study highlight the importance of considering separate injury severity models for different age groups, specifically separating older drivers from others, as the difference among older drivers and others are substantial.
Keywords: Age Difference; Single-Vehicle Crash; Rural Two-Lane Highways; Generalized Ordered Logit Model

References

Journal of Geotechnical and Transportation Engineering - 2017 vol. 3 (1)

[1] Wu Q, Chen F, Zhang G, Liu XC, Wang H, Bogus SM. Mixed logit model-based driver injury severity investigations in single-and multi-vehicle crashes on rural two-lane highways. Accident Analysis & Prevention. 2014 Nov 30;72:105-15.
[2] Khorashadi A, Niemeier D, Shankar V, Mannering F. Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. Accident Analysis & Prevention. 2005 Sep 30;37(5):910-21.
[3] Critical Analysis Reporting Environment. Alabama Traffic Crash Statistics. http://care.cs.ua.edu. Accessed September 02, 2016.
[4] Texas Department of Transportation (TxDOT), Texas Motor vehicle Crash Statistics, 2014.
[5] Arizona Department of Transportation (ADOT), Arizona Motor Vehicle Crash Facts, 2012.
[6] Kentucky Transportation Center (KTC), Kentucky Traffic Collision Facts, 2012.
[7] New Mexico Department of Transportation (NMDOT), New Mexico Traffic Crash Annual Report, 2012.
[8] National Highway Traffic Safety Administration, Fatality Analysis Reporting System (FARS) Encyclopedia, Washington, D.C., http://www-fars.nhtsa.dot.gov/main/index.aspx. Accessed March 14, 2017.
[9] De Ona J, Mujalli RO, Calvo FJ. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident Analysis & Prevention. 2011 Jan 31;43(1):402-11.
[10] Xie Y, Zhao K, Huynh N. Analysis of driver injury severity in rural single-vehicle crashes. Accident Analysis & Prevention. 2012 Jul 31;47:36-44.
[11] Kim JK, Ulfarsson GF, Kim S, Shankar VN. Driver-injury severity in single-vehicle crashes in California: a mixed logit analysis of heterogeneity due to age and gender. Accident Analysis & Prevention. 2013 Jan 31;50:1073-81.
[12] Lopez G, Abellan J, Montella A, De Ona J. Patterns of single-vehicle crashes on two-lane rural highways in Granada province, Spain: in-depth analysis through decision rules. Transportation Research Record: Journal of the Transportation Research Board. 2014 Nov 5(2432):133-41.
[13] Abay, K. A., Paleti, R., & Bhat, C. R. (2013). The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity. Transportation research part B: methodological, 50, 74-89.
[14] Morgan, A., & Mannering, F. L. (2011). The effects of road-surface conditions, age, and gender on driver-injury severities. Accident Analysis & Prevention, 43(5), 1852-1863.
[15] Liu, C., Utter, D., & Chen, C. (2007). Characteristics of Crash Injuries Among Young, Middle-aged, and Older Drivers. National Highway Traffic Safety Administration Technical Report DOT HS 810 857, Washington, D.C.
[16] Khattak AJ, Pawlovich MD, Souleyrette RR, Hallmark SL. Factors related to more severe older driver traffic crash injuries. Journal of Transportation Engineering. 2002 May;128(3):243-9.
[17] Dissanayake S, Lu JJ. Factors influential in making an injury severity difference to older drivers involved in fixed object-passenger car crashes. Accident Analysis & Prevention. 2002 Sep 30;34(5):609-18.
[18] Awadzi KD, Classen S, Hall A, Duncan RP, Garvan CW. Predictors of injury among younger and older adults in fatal motor vehicle crashes. Accident Analysis & Prevention. 2008 Nov 30;40(6):1804-10.
[19] Zhang C, Ivan J. Effects of geometric characteristics on head-on crash incidence on two-lane roads in Connecticut. Transportation Research Record: Journal of the Transportation Research Board. 2005 Jan 1(1908):159-64.
[20] Boufous S, Finch C, Hayen A, Williamson A. The impact of environmental, vehicle and driver characteristics on injury severity in older drivers hospitalized as a result of a traffic crash. Journal of safety research. 2008 Dec 31;39(1):65-72.
[21] Hao W, Kamga C, Daniel J. The effect of age and gender on motor vehicle driver injury severity at highway-rail grade crossings in the United States. Journal of safety research. 2015 Dec 31;55:105-13.
[22] Perera L, Dissanayake S. Contributing Factors to Older-Driver Injury Severity in Rural and Urban Areas. In Journal of the transportation research forum 2012 Aug 20 (Vol. 49, No. 1).
[23] Wang S. Accident Severity and Young and Old Drivers (Doctoral dissertation, University of Ottawa).
[24] Gray RC, Quddus MA, Evans A. Injury severity analysis of accidents involving young male drivers in Great Britain. Journal of Safety Research. 2008 Dec 31;39(5):483-95.
[25] Lam LT, Norton R, Woodward M, Connor J, Ameratunga S. Passenger carriage and car crash injury: a comparison between younger and older drivers. Accident Analysis & Prevention. 2003 Nov 30;35(6):861-7.
[26] Weiss HB, Kaplan S, Prato CG. Analysis of factors associated with injury severity in crashes involving young New Zealand drivers. Accident Analysis & Prevention. 2014 Apr 30;65:142-55.
[27] Mao Y, Zhang J, Robbins G, Clarke K, Lam M, Pickett W. Factors affecting the severity of motor vehicle traffic crashes involving young drivers in Ontario. Injury Prevention. 1997 Sep 1;3(3):183-9.
[28] Pour-Rouholamin M, Zhou H, Zhang B, Turochy RE. Comprehensive analysis of Wrong-Way driving crashes on Alabama interstates. In Transportation Research Board 95th Annual Meeting 2016 (No. 16-3999).
[29] Baireddy, R., Pour-Rouholamin, M., Zhou, H., & Qi, Y. (2017). Factors Contributing to Injury Severity of Pedestrian Crashes at Uncontrolled Locations in Illinois (No. 17-03502).
[30] Pour-Rouholamin, M. Wrong-Way Driving and Injury Severity: An Empirical Investigation. 2017. Available at SSRN: https://ssrn.com/abstract=2922666
[31] Pour-Rouholamin M, Zhou H. Investigating the risk factors associated with pedestrian injury severity in Illinois. Journal of safety research. 2016 Jun 30;57:9-17.
[32] Pour-Rouholamin M, Zhou H. Analysis of driver injury severity in wrong-way driving crashes on controlled-access highways. Accident Analysis & Prevention. 2016 Sep 30;94:80-8.
[33] Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990 Dec 1:1171-8.
[34] Pour-Rouholamin, M. (2016). Wrong-Way Driving: Crash Data Analysis and Safety Countermeasures (Doctoral dissertation, Auburn University).
[35] Kockelman KM, Kweon YJ. Driver injury severity: an application of ordered probit models. Accident Analysis & Prevention. 2002 May 31;34(3):313-21.
[36] Pour-Rouholamin, M., Zhou, H., & Shaw, J. (2014). Overview of safety countermeasures for wrong-way driving crashes. Institute of Transportation Engineers. ITE Journal, 84(12), 31.
[37] Harootunian K, Lee BH, Aultman-Hall L. Odds of fault and factors for out-of-state drivers in crashes in four states of the USA. Accident Analysis & Prevention. 2014 Nov 30;72:32-43.
[38] Zhou H, Pour-Rouholamin, M. (2014). Guidelines for reducing wrong-way crashes on freeways. Illinois Center for Transportation/Illinois Department of Transportation.
[39] Sivak M, Schoettle B. Update: Percentage of young persons with a driver's license continues to drop. Traffic injury prevention. 2012 Jul 1;13(4):341-.
[40] Ulfarsson GF, Mannering FL. Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accident Analysis & Prevention. 2004 Mar 31;36(2):135-47.
[41] Zhou H, Zhao J, Pour-Rouholamin M, Tobias PA. Statistical characteristics of wrong-way driving crashes on Illinois freeways. Traffic injury prevention. 2015 Nov 17;16(8):760-7.
[42] Gkritza K, Mannering FL. Mixed logit analysis of safety-belt use in single-and multi-occupant vehicles. Accident Analysis & Prevention. 2008 Mar 31;40(2):443-51.
[43] Yau KK. Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong. Accident Analysis & Prevention. 2004 May 31;36(3):333-40.
[44] McCartt AT, Teoh ER. Type, size and age of vehicles driven by teenage drivers killed in crashes during 2008-2012. Injury prevention. 2015 Apr 1;21(2):133-6.
[45] Li X. Analysis of Injury Severity of Drivers Involved in Single-Vehicle and Two-Vehicle Crashes on Ontario Highways. 2014.
[46] Pour-Rouholamin, M., Zhou, H., Shaw, J., & Tobias, P. (2015). Current Practices of Safety Countermeasures for Wrong-Way Driving Crashes. In Transportation Research Board 94th Annual Meeting (No. 15-3648).
[47] Jiang X, Huang B, Zaretzki RL, Richards S, Yan X, Zhang H. Investigating the influence of curbs on single-vehicle crash injury severity utilizing zero-inflated ordered probit models. Accident Analysis & Prevention. 2013 Aug 31;57:55-66.
[48] Shaheed MS, Gkritza K. A latent class analysis of single-vehicle motorcycle crash severity outcomes. Analytic methods in accident research. 2014 Apr 30;2:30-8.
[49] Amarasingha N, Dissanayake S. Gender differences of young drivers on injury severity outcome of highway crashes. Journal of safety research. 2014 Jun 30;49:113-e1.
[50] Yasmin S, Eluru N, Pinjari AR, Tay R. Examining driver injury severity in two vehicle crashes-A copula based approach. Accident Analysis & Prevention. 2014 May 31;66:120-35.

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Network-Based Optimization of Traffic Signals Timing Using Internal Metering Policy by Paying Attention to Upstream Intersections

Mina Ghanbari Karekani, Mohammad Mehdi Barzegar Ganji, Ali Arab

Abstract
Timing actuated traffic signals is usually done by methodologies considering minimizing delay times, stop times, queue length etc. as the core idea. In none of such methodologies the effects of other adjacent intersections are considered. In other words, there is no control over queue congestion in such methodologies. In the proposed algorithm of traffic signs optimization, using an internal (RT/IMPOST) metering policy maintains an optimum queue length ratio within the network. In this method, the effect of upstream intersection is considered in timing procedure. In this paper a traffic signals optimization method is elaborated and proposed for the whole network and has been particularized in three aspects: 1) The queue length ratio within the whole network: in this aspect, the queue length ratio has been conserved within an optimum interval 2) the queue time ratio: the time ratio of vehicles movement in congested condition to the free flow condition has been restricted and 3) the queue speed ratio: in this case the proportion of vehicles speed in congested condition to free flow speed has been restricted and conserved. The aim of these algorithms is to minimize the difference of queue length, time and speed ratios with their corresponding optimum values which could lead into a more optimized timing and determining efficient green phase timing for each intersection. With comparison of the results taken from each of these three algorithms, it was cleared up that queue length ratio algorithm has better results for oversaturated networks and could ameliorate the traffic conditions better compared to the other algorithms.
Keywords: Network internal metering, queue length ratio algorithm, queue time ratio algorithm, queue speed ratio algorithm, traffic signals coordination

References

Journal of Geotechnical and Transportation Engineering - 2017 vol. 3 (1)

[1] R. Foroughi, GH. A. Montazer, R. Sabzevari. Design of a New Urban Traffic Control System Using Modified Ant Colony Optimization Approach, Iranian Journal of Science & Technology, Transaction B, Engineering, Vol. 32, No. B2, pp. 167-173, 2007.
[2] A. H. Ghods, A. Rahimi-Kian, An Efficient Optimization Approach to Real-Time Coordinated and Integrated Freeway Traffic Control, IEEE Transactions on Intelligent Transportation Systems, 2010.
[3] Q. Derek, A Review of Queue Management Strategies, ITS University of Leeds, 1992.
[4] W. Cheng, D. Wang, Y. Chen, M. Yuan, X. Li., Research on Signal Control Methods of Traffic Bottlenecks in City Road Network, IEEE computer society, DOI 10.1109/GCIS.416, 2009.
[5] F. V. Webster, Traffic Signal Settings, Road Research Technical, Paper No. 39.
[6] R. L. Gordon, A technique for control of traffic at critical intersections, Transportation Science 4, pp. 279-287, 1969.
[7] H. T. Inosi. Road Traffic Control, 1975.
[8] R. Nagui, A. Rahmi, Paired Intersections: Initial Development of Platooned Arrival and Queue Interaction Models, Australian Road Research Board, 1991.
[9] D. C. Gazis, Optimum control of a system of oversaturated Intersections, Operations Research 12, pp. 815-831, 1964.
[10] A. Gal-Tzur, D. Mahalel, J. N. Prashker, Signal design for congested networks based on metering, 1993.
[11] H. C. Liu, K. Masao, A Study on Real-Time Signal Control for an Oversaturated Network.
[12] Y. Toshio, Y. Yuji, K. Ryuichi, "Evaluation of an Area Metering Control Method Using the Macroscopic Fundamental Diagram", Lisbon, Portugal, 2010.
[13] N. H. Gartner, S. F. Assman, F. Lasaga, D. L. Hou, A multi-band approach to arterial traffic signal optimization, Elsevier Ltd, 2002.
[14] A. P. Akgungor, A New Delay Parameter Dependent on Variable Analysis Periods at Signalized Intersections. Part 1: Model Development, Transport 23(1), pp. 31-36, 2008
[15] H. Praneviciusa, T. Kraujalisb, Knowledge based traffic signal control model for signalized intersection, Transport 27(3), pp. 263-267, 2012
[16] P. E. B. Lieberman, J. Chang, E. Shenk Prassas, Formulation of a Real-Time Control Policy for Oversaturated Arterials, 2000.

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Managing Heavy Vehicle Drivers Fatigue: A Critical Review of the Literature and Safe System Interventions

Mozhgan Alaiakbari, Sara Moridpour

Abstract
According to the Australian Work Health and Safety Strategy 2012-2022, the prevention of road transport accidents has been identified as a national priority, and efforts have been made by national regulators to prevent heavy vehicle related workplace incidents and accidents. In recent years, much attention has been given to heavy vehicle drivers fatigue management. Increasing evidence suggests that long working hours negatively influence heavy vehicle drivers physiology, health, and safety. However, there is little empirical research in the heavy vehicle transport sector in Australia to identify the influence of working hours management on drivers fatigue and consequently, on the risk of crashes and injuries. There is no national legislation regulating the number of hours or kilometres travelled by heavy vehicle drivers. Consequently, it is almost impossible to define a standard number of hours or kilometres for heavy vehicle drivers in a safety management system. This paper reviews the existing studies concerning safe system interventions such as tachographs in relation to fatigue caused by long working hours. This paper also reviews the literature to identify the influence of frequency of rest breaks on the reduction of work-related road transport accidents involving heavy vehicles. A framework from existing studies is presented to manage heavy vehicle drivers fatigue, which may result in the reduction of injuries and fatalities involving heavy vehicles.
Keywords: Heavy vehicle; Fatigue; Tachographs.

References

Journal of Geotechnical and Transportation Engineering - 2017 vol. 3 (1)

[1] ILO (international Labour Office Geneva), The issue of Fatigue and Working time in the road transport sector, pp. 1-25, 2005.
[2] Akhtar, M., & Bouwer Utne, I. , Common patterns in aggregated accident analysis charts from human fatigue-related groundings and collisions at sea. Maritime Policy & Management, 42(2), pp. 186-206, 2014. http://dx.doi.org/10.1080/03088839.2014.926032
[3] Williamson, A., Lombardi, D., Folkard, S., Stutts, J., Courtney, T., & Connor, J. , The link between fatigue and safety. Accident Analysis & Prevention, 43(2), pp. 498-515, 2011
[4] Rabinbach, A. ,The Human Motor: Energy, Fatigue and the Origins of Modernity , International Labor and Working-Class History, No. 41 ,pp. 89-91, 1992
[5] Dawson, D., Chapman, J., & Thomas, M. , Fatigue-proofing: A new approach to reducing fatigue-related risk using the principles of error management. Sleep Medicine Reviews, 16(2), pp. 167-175, 2012 http://dx.doi.org/10.1016/j.smrv.2011.05.004
[6] James, P. , Health and Safety At Work and Its Relevance to Employment Relations Research. Bingley, United Kingdom: Emerald Group Publishing,2006
[7] Laird, P. & Bachels, M. , Back on Track: Rethinking Transport Policy in Australia and New Zealand. Sydney, Australia: UNSW Press, 2001
[8] Tovey, M. , Design for Transport: A User-Centred Approach to Vehicle Design and Travel. Surrey, England: Gower Publishing, Ltd, 2013.
[9] Mayhew, C., & Quinlan, M, Economic pressure, multi-tiered subcontracting and occupational health and safety in Australian long-haul trucking. Employee Rrelations, 28(3), pp. 212-229, 2006
[10] Pape, D. , Role of Human Factors in Preventing Cargo Tank Truck Rollovers. Washington, DC: Transportation Research Board, 2012.
[11] OECD. Safety on Roads What's the Vision?: What's the Vision?. Danvers, MA: OECD Publishing, 2002.
[12] De Smet, A. (2008). Transportation Accident Analysis and Prevention. New York, NY: Nova Publishers.
[13] Morrow, P. C., & Crum, M. R., Antecedents of fatigue, close calls, and crashes among commercial motor-vehicle drivers. Journal of Ssafety Rresearch, 35(1), pp. 59-69, 2004.
[14] Acton, A. , Issues in Computer Science and Theory: 2013 Edition. Atlanta, GA: Scholarly Editions, 2013
[15] Crum, M. R., & Morrow, P. C. , The influence of carrier scheduling practices on truck driver fatigue. Transportation Journal, 42, pp. 20-41,2002.
[16] Kjellen, U., Prevention of Accidents Through Experience Feedback. New York, NY: CRC Press, 2002
[17] Langan-Fox, J. & Cooper, C, Handbook of Stress in the Occupations. Northampton, MA: Edward Elgar Publishing,pp. 431-451, 2011.
[18] Porter, B. , Handbook of Traffic Psychology. Waltham, MA: Academic Press, pp. 403-441,2011.
[19] Amundsen, A. H., & Sagberg, F., Hours of service regulations and the risk of fatigue-and sleep-related road accidents. A literature review Report, pp. 659, 2033.
[20] Boyd, C., Human Resource Management and Occupational Health and Safety. New York, NY: Routledge, pp. 3-140, 2004
[21] Du Plessis, S., Agarwal, A. & Sabanegh, E. Jr. , Male Infertility: A Complete Guide to Lifestyle and Environmental Factors. New York, NY: Springer. 2014
[22] Pfeffer, J. (2013). What Were They Thinking? Unconventional Wisdom About Management. Boston, MA: Harvard Business Press,2013
[23] Lemke, Paar, & Wolf. (2006). Embedded Security in Cars: Securing Current and Future Automotive IT Applications. New York, NY: Springer Science & Business Media.
[24] Hamilton, S. Trucking Country: The Road to America's Wal-Mart Economy. Princeton, NJ: Princeton University Press,2008
[25] Anderson, M. , Contemporary Ergonomics and Human Factors , Proceedings of the International Conference on Ergonomics & Human Factors 2013, Cambridge, UK, pp. 15-18 , April 2013.
[26] New York, NY: Taylor & Francis.
[27] Verster, J., Pandi-Perumal, S., Ramaekers, J., & De Gier, J., Drugs, Driving and Traffic Safety. Germany: Springer Science & Business Media, 2009.
[28] Safe Work Australia , 2012, Australian Work Health and Safety Strategy 2012 to 2022,
[29] Trombly, J., Dealing with Truck Parking Demands. Washington, DC: Transportation Research Board,2003.

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Prioritizing Urban Corridors for Multilevelization Using Benefit-Cost Analysis

Amirhossein Baghestani, Babak Mirbaha, Sheida Roshankhah, Alireza Mahpour

Abstract
Providing sufficient supply considering future demand is one of transportation management policies these days. Urban multilevel highways can positively affect congested streets by increasing network operational speed and decreasing delay time though it is costly. The current paper aims to present a methodology for comparing the multilevelization capability of urban corridors considering transportation and economic parameters. Mashhad which is one of the main cities of Iran is chosen as the case study. Primary corridors are identified among the most effective main streets of the city. As a result, 6 selected corridors are modeled in a macroscopic software and analyzed. Benefit assessment is done based on two factors: network total traveled time (Vehicle. Hour of travel) and network total traveled distance (Vehicle. Kilometer). Next, the benefit to cost ratio is calculated for a 10-years period employing VHT reduction and decrease in fuel consumption and air pollution as benefits, and construction and maintenance costs as cost. The results show that the best scenario is multileveling Ferdowsi street.
Keywords: Multilevel corridors, Benefit to cost, VHT, fuel consumption, air pollution

References

Journal of Geotechnical and Transportation Engineering - 2017 vol. 3 (1)

[1] Road projects cost benefit analysis: scenario analysis of the effect of varying inputs, Technical Report, The World Bank,The International Bank for Reconstruction and Development, Department for International Development (DFID), Washington D.C., 2010.
[2] Harberger A C, Introduction to cost- benefit analysis applications to highway projects, University of California, Los Angeles, CL, 2009.
[3] Samuel P, Robert W and Poole J, Innovative roadway design making highways more likeable, Reason Foundation Policy Study, 348, 2006.
[4] Shepard R, Georgiadis A and Linn J, Assessment of the Miami urban watch alternative for rebuilding I-395, The University of Miami, School of Architecture, Center for Urban and Community Design, Miami, FL, 2002.
[5] Buffington J L, Vadali S R, Womack K N, Zimmer R A, McCully. W G, Nikolaou. M, Lewis C A, Social, economic, and environmental effects of elevated, depressed, and at-grade level freeways in Texas, Research Report, Texas Transportation Institute, College Station, TX, 1327-6F, 1997.
[6] Choi J, Cheonggyecheon restoration project: a revolution in Seoul, Seoul, Korea, 2006.
[7] Cervero R, Freeway deconstruction and urban regeneration in the United States, International Symposium for the 1st Anniversary of the Cheonggyecheon Restoration, Seoul, Korea, 2006.
[8] Learning from the big dig (Daniel C. Wood). July/August 2001 Retrieved from http://www.fhwa.dot.gov/publications/publicroads/01julaug/bigdig.cfm
[9] Cost benefit parameters and application rules for transport project appraisal, Technical Report, Goodbody Economic Consultants in Association with ATKINS, Dublin, Ireland, 2004.
[10] Fegan O, Cost-benefit analysis of the Dublin Luas light rail project, Student Economic Review, Vol. 17, pp. 213- 224, 2003.
[11] Approaches to Making Federal Highway Spending More Productive, CONGRESSIONAL BUDGET OFFICE, CONGRESS OF THE UNITED STATES, 2016.
[12] Benefit-cost analysis for transportation projects. 2016 Retrieved from http://www.dot.state.mn.us/planning/program/benefitcost.html, 2016
[13] Mashhad comprehensive studies update, assessing suggested solutions for network improvement, Tarh Haftom Consultants, Tehran, Iran, 2010.
[14] Mashhad comprehensive studies update, economic evaluation of suggested solutions for network improvement, Tarh Haftom Consultants, Tehran, Iran, 2011.

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A quantitative impact analysis of attitudes towards safety and traffic on high school students walking to school

Iraj Firouzi, Amir Reza Mamdoohi

Abstract
An increase in motorized mode for school travels has a result of morning traffic and students physical inactivity. Although, some research has been conducted was done considering economic, social and environmental parameters of students travel behavior, but the effect of attitudes for mode choice was not considered significantly. The purpose of this research is to discuss effects of attitude towards safety and traffic together with economic and social parameters on high school mode choice (specially walking). To collect the economic and social data and investigation of safety and traffics attitudes, written questionnaires were distributed among 656 students of 7 high schools (public and private, girls and boys) in two educational districts of Kerman. Choosing case samples were done classified and randomly. A multinomial logit model was applied for investigation of different parameters effects on mode choice for school trip. to decrease safety and traffic attitudes variables, an exploratory factor analysis was applied. Results show that students which have no accessibility to public transportation and evaluate highly their Safety knowledge are walking to school with more possibility.
Keywords: School mode choice, High school studentsLogit model, Attitude, Factor analysis

References

Journal of Geotechnical and Transportation Engineering - 2017 vol. 3 (1)

[1] R. Mitra, Independent Mobility and Mode Choice for School Transportation: A Review and Framework for Future Research Independent Mobility and Mode Choice for School Transportation: A Review and Framework, Transp. Rev., vol. 33, no. 1, pp. 21 to 43, 2013.
[2] N. C. McDonald, Childrens mode choice for the school trip: The role of distance and school location in walking to school, Transportation (Amst)., vol. 35, no. 1, pp. 23 to 35, 2008.
[3] M. Oliver, H. Badland, S. Mavoa, K. Witten, R. Kearns, A. Ellaway, E. Hinckson, L. Mackay, and P. Schluter, Environmental and socio-demographic associates of childrens active transport to school: a cross-sectional investigation from the URBAN Study, Int. J. Behav. Nutr. Phys. Act., vol. 11, no. 1, pp. 70 to 82, 2014.
[4] A. Broberg and S. Sarjala, School travel mode choice and the characteristics of the urban built environment: The case of Helsinki, Finland, Transp. Policy, vol. 37, pp. 1 to 10, 2015.
[5] R. Mitra and R. N. Buliung, Exploring differences in school travel mode choice behaviour between children and youth, Transp. Policy, vol. 42, pp. 4 to 11, 2015.
[6] O. Stewart, Findings from research on active transportation to school and implications for safe routes to school programs, J. Plan. Lit., vol. 26, no. 2, pp. 127 to 150, 2011.
[7] S. L. Martin, S. M. Lee, and R. Lowry, National prevalence and correlates of walking and bicycling to school, Am. J. Prev. Med., vol. 33, no. 2, pp. 98 to 105, 2007.
[8] L. Grize, B. Bringolf-Isler, E. Martin, and C. Braun-Fahrlander, Research Trend in active transportation to school among Swiss school children and its associated factors: three cross-sectional surveys 1994, 2000 and 2005, Int. J. Behav. Nutr. Phys. Act., vol. 28, no. 7, pp. 1 to 8, 2010.
[9] A. Ermagun and A. Samimi, Promoting active transportation modes in school trips, Transp. Policy, vol. 37, pp. 203 to 211, 2015.
[10] N. C. McDonald, Active transportation to school: trends among US schoolchildren, 1969 to 2001, Am. J. Prev. Med., vol. 32, no. 6, pp. 509 to 516, 2007.
[11] R. N. Buliung, R. Mitra, and G. Faulkner, Active school transportation in the Greater Toronto Area, Canada: an exploration of trends in space and time (1986-2006), Prev. Med. (Baltim)., vol. 48, no. 6, pp. 507 to 512, 2009.
[12] H. P. Van der Ploeg, D. Merom, G. Corpuz, and A. E. Bauman, Trends in Australian children traveling to school 1971 to 2003: burning petrol or carbohydrates?, Prev. Med. (Baltim)., vol. 46, no. 1, pp. 60 to 62, 2008.
[13] S. Easton and E. Ferrari, Childrens travel to school the interaction of individual, neighbourhood and school factors, Transp. Policy, vol. 44, pp. 9 to 18, 2015.
[14] M. Mehdizadeh, A. R. Mamdoohi, M. Fallah Zavareh, A Mode Choice Model for Elementary School Trips Based on Cultural Variables (A Case Study), The 13th International Conference on Traffic and Transportation Engineering (in Persian). 2014.
[15] T. E. McMillan, The relative influence of urban form on a childs travel mode to school, Transp. Res. Part A Policy Pract., vol. 41, no. 1, pp. 69 to 79, 2007.
[16] T. McMillan, K. Day, M. Boarnet, M. Alfonzo, and C. Anderson, Johnny walks to school does Jane? Sex differences in childrens active travel to school, Child. Youth Environ., vol. 16, no. 1, pp. 75 to 89, 2006.
[17] B.-E. Moen, Risk perception, priority of safety and demand for risk mitigation in transport, 2008.
[18] H. Iversen and T. Rundmo, Attitudes towards traffic safety, driving behaviour and accident involvement among the Norwegian public., Ergonomics, vol. 47, no. 5, pp. 555 to 572, 2004.
[19] T. Rundmo, T. Nordfjern, H. H. Iversen, S. Oltedal, and S. H. Jorgensen, The role of risk perception and other risk-related judgements in transportation mode use, Saf. Sci., vol. 49, no. 2, pp. 226 to 235, Feb. 2011.
[20] H. Abdi and L. J. Williams, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat., vol. 2, no. 4, pp. 433 to 459, 2010.
[21] K. E. Train, Discrete choice methods with simulation. Cambridge university press, 2009.

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