Article
WALKING FEEDER MODE SERVICES CHOICE ANALYSIS FOR INTEGRATION OF BUS RAPID TRANSIT SYSTEM: A CASE STUDY
DOI: 10.7708/ijtte.2017.7(4).07
7 / 4 / 487-497 Pages
Author(s)
Manjurali Imadadali Balya - Sardar Vallabhbhai National Institute of Technology, Surat, India -
Abstract
Public transit system plays a vital role in economic development for access to the commuters in urban locations. To ensure accessibility of public transit system is a daunting task faced by developing countries due to the lack of feeder services. The present study examines research on proposed feeder mode service choices as walking as one mode with three optional service categories such as Exclusive Footpath Service (EFS), Special Shoulder Treatment for Pedestrian only (SSTP) and Pedestrian allow in Mixed Traffic (PMT) services. The Multinomial Logit (MNL) Model is used to analyze the proposed integrated services for Bus Rapid Transit System corridor of Ahmedabad City in SPSS environment. The findings revealed the increase in travel time and travel distance causes the user's choice decrease to the proposed services and shift to personal vehicles for access to the stops. The prediction accuracy of the proposed model is 49.4% which is greater than the proportional by chance accuracy criteria of 47.9% indicates the model was satisfied. The output of this research paper is based on case study, but the proposed methodology can apply as base for introducing the proposed walking feeder mode services of the same sized metropolitan cities.
Number of downloads: 1362
Keywords:
integrated walking feeder mode services;
walking;
bus rapid transit system (BRTS);
multinomial logit (MNL) model;
References:
Ashalatha, R.; Manju, V.; Zacharia, A.B. 2012. Mode Choice Behavior of Commuters in Thiruvananthapuram City, Journal of Transportation Engineering 139(5): 494-502.
Ashiabor, S.; Baik, H.; Trani, A. 2007. Logit Models for Forecasting Nationwide Intercity Travel Demand in the United States, Transportation Research Record: Journal of the Transportation Research Board: 1-12.
Balya, M.I.; Kumar, R. 2017. Proposed Integrated Bicycle Feeder Mode Service Analysis for Bus Rapid Transit System-A Multinomial Logit Model Approach, International Journal for Traffic and Transport Engineering 7(1): 108-116.
Bayaga, A. 2010. Multinomial Logistic Regression: Usage and Application in Risk Analysis, Journal of applied quantitative methods 5(2): 288-297.
Cervero, R. 2001. Walk-And-Ride: Factors Influencing Pedestrian Access to Transit, Journal of Public Transportation 7(3): 1-23.
Chandra, S.; Bari, M.E.; Devarasetty, P.C.; Vadali, S. 2013. Accessibility Evaluations of Feeder Transit Services, Transportation Research Part A: Policy and Practice 52: 47-63.
Chien, S.; Yang, Z. 2000. Optimal Feeder Bus Routes on Irregular Street Networks, Journal of Advanced Transportation 34(2): 213-248.
Das, S.S.; Maitra, B.; Boltze, M. 2012. Planning of Fixed-Route Fixed-Schedule Feeder Service to Bus Stops in Rural India, Journal of Transportation Engineering 138(10): 1274-1281.
El-Habil, A.M. 2012. An Application on Multinomial Logistic Regression Model, Pakistan Journal of Statistics and Operation Research 8(2): 271-291.
Kumar, R.; Electricwala, F. 2014. Impact of Proposed Modal Shift from Private Users to Bus Rapid Transit System: An Indian City Case Study, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering 8(6): 663-667.
Loutzenheiser, D. 1997. Pedestrian Access to Transit: Model of Walk Trips and Their Design and Urban Form Determinants Around Bay Area Rapid Transit Stations, Transportation Research Record: Journal of the Transportation Research Board 1604: 40-49.
Martínez, L.M. Eiró, T. 2012. An Optimization Procedure to Design a Minibus Feeder Service: An Application to the Sintra Rail Line, Procedia-Social and Behavioral Sciences 54: 525-536.
Miskeen, A.; Alhodairi, A.M.; Rahmat, R. 2013a. Modeling a Multinomial Logit Model of Intercity Travel Mode Choice Behavior for All Trips in Libya, International Journal of Civil, Architectural Science and Engineering 7(9): 1-10.
Miskeen, A.; Manssour, A.; Mohamed Alhodairi, A.; Rahmat, R. 2013b. Modeling of Intercity Transport Mode Choice Behavior in Libya: a Binary Logit Model for Business Trips by Private Car and Intercity Bus, Australian Journal of Basic & Applied Sciences 7(1): 302-311.
Mohaymany, A.S.; Gholami, A. 2010. Multimodal Feeder Network Design Problem: Ant Colony Optimization Approach, Journal of Transportation Engineering 136(4): 323-331.
Mukala, P.K.; Chunchu, M. 2011. Mode Choice Modelling for Intercity Transportation in India: A Case of Guwahati to Five Metro Cities, International Journal of Earth Sciences and Engineering 4(6): 364-374.
Neter, J. 1983. Applied Linear Regression Models. R.D. Irwin-Open Library.
Polzin, S.; Chu, X.; Rey, J. 2000. Density and Captivity in Public Transit Success: Observations from the 1995 Nationwide Personal Transportation Study, Transportation Research Record: Journal of the Transportation Research Board (1735): 10-18.
Rahman, M. S.-U.; Timms, P.; Montgomery, F. 2012. Integrating BRT Systems With Rickshaws in Developing Cities to Promote Energy Efficient Travel, Procedia-Social and Behavioral Sciences 54:261-274.
Replogle, M. 1992. Bicycles and Cycle-Rickshaws in Asian Cities: Issues and Strategies, Transportation Research Record 1372: 76-84.
Rudnicki, A. Equivalent Travel Time of Passengers as a Synthetic Performance Measure in Urban Public Transport. In Proceedings from the 2nd KFB-Research Conference on Urban Transport Systems, 174-183.
Shrivastava, P.; O’Mahony, M. 2006. A Model for Development of Optimized Feeder Routes and Coordinated Schedules-A Genetic Algorithms Approach, Transport policy 13(5): 413-425.
Stringham, M. 1982. Travel Behavior Associated with Land Uses Adjacent to Rapid Transit Stations, Institute of Transportation Engineers Journal 52(4): 18-22.
Verma, A.; Dhingra, S. 2005. Feeder Bus Routes Generation within Integrated Mass Transit Planning Framework, Journal of Transportation Engineering 131(11): 822-834.
Wedagama, D.P. 2009. A Multinomial Logit Model for Estimating the Influence of Household Characteristics on Motorcycle Ownership: A Case Study in Denpasar City, Bali, Journal of Civil Engineering 29(1): 2-9.
White, J. 2013. Logistic Regression Model Effectiveness: Proportional Chance Criteria and Proportional Reduction in Error, Journal of Contemporary Research in Education 2(1): 4-10.
Wibowo, S.S.; Olszewski, P. 2005. Modeling Walking Accessibility to Public Transport Terminals: Case Study of Singapore Mass Rapid Transit, Journal of the Eastern Asia Society for Transportation Studies 6: 147-156.