By A. Abdel-Rahim
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Additional resources for Intelligent Transportation Systems
This expansion is doomed to be unprofitable. Other typical problems of this “organic growth” are also shown: the purple route develops an irrational pathway; the black route has two (2) variations (say “Black a” and “Black b”), which means that the frequencies at the end points are unsatisfactory; and there is an oversupply of capacity at the route endings. If the city plans to develop new districts, with five (5) new zones at specific locations, each zone will ITS Applications in Developing Countries: A Case Study of Bus Rapid Transit and Mobility Management Strategies in Dar es Salaam – Tanzania 47 need several new routes to reach the existing city areas, including a route that offers a direct link between the zones themselves.
Texas Transportation Institute. Travel Time Data Collection Handbook, Report No. , (2006). 278-287. Recker, W. , Horrocks, M. & Oh J. (2005). Considering Risk-Taking Behavior in Travel Time Reliability, California PATH Research Report. Sen, S. & Pillai, R. (2001). A Mean-Variance Model for Route Guidance in Advanced Traveler Information Systems, Transportation Science 35, pp. 37-49. Zhou, Z. (2008). Models and Algorithms for Addressing Travel Time Variability: Applications from Optimal Path Finding and Traffic Equilibrium Problems.
The demand for transport service is highly qualitative and differentiated. There is a whole range of specific demands for transport which are differentiated by time of day, day of week, journey purpose, type of cargo, importance of speed and frequency, and so on. A transport service without the attribute matching this differentiated demand may well be useless. The demand for transport is derived; it is not an end in itself. With the possible exception of sightseeing, people travel in order to satisfy a need (work, leisure, education, health) at their destination.