Investigating Acceptable Level of Travel Demand before Capacity Enhancement for Signalized Urban Road Networks

Abstract

Increasing travel demand in urban areas triggers traffic congestion and increases delay in road networks. In this context, local authorities that are responsible for traffic operations seek to strike a balance between traffic volume and capacity to reduce total travel time on road networks. Since intersections are the most critical components of road networks in terms of safety and operational issues, adjusting intersection signal timings becomes an effective method for authorities. When this tool remains incapable of overcoming traffic congestions, authorities take expensive measures such as increasing link capacities, lane additions or applying grade-separated junctions. However, it may be more useful to handle road networks as a whole by investigating the effects of optimizing signal timings of all intersections in the network. Therefore, it would be useful to investigate the right time for physical improvements on the road network to avoid premature investments considering limited resources of local authorities. In this study, effects of increasing travel demand on Total Travel Cost (TTC) is investigated by developing a bi-level programming model, called TRAvel COst Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the stochastic user equilibrium link flows determined at the lower level. The TRACOM is applied to Allsop and Charlesworths’ network for different origin-destination demand matrix multipliers. Results revealed that TTC values showed an approximate linear increase while the travel demand is increased up to 16%. After this value, TTC showed a sudden spike although the travel demand was linearly increased that means optimizing signal timings must be supported by applying psychical improvements.

Keywords

• Total travel cost,urban road networks,signal timing optimization,investment decisions

Investigating Acceptable Level of Travel Demand Before Capacity Enhancement for Signalized Urban Road Networks

Abstract

Increasing travel demand in urban areas triggers traffic congestion and increases delay in road networks. In this context, local authorities that are responsible for traffic operations seek to strike a balance between traffic volume and capacity to reduce total travel time on road networks. Since intersections are the most critical components of road networks in terms of safety and operational issues, adjusting intersection signal timings becomes an effective method for authorities. When this tool remains incapable of overcoming traffic congestions, authorities take expensive measures such as increasing link capacities, lane additions or applying grade-separated junctions. However, it may be more useful to handle road networks as a whole by investigating the effects of optimizing signal timings of all intersections in the network. Therefore, it would be useful to investigate the right time for physical improvements on the road network to avoid premature investments considering limited resources of local authorities. In this study, effects of increasing travel demand on Total Travel Cost (TTC) is investigated by developing a bi-level programming model, called TRAvel COst Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the stochastic user equilibrium link flows determined at the lower level. The TRACOM is applied to Allsop and Charlesworths’ network for different origin-destination demand matrix multipliers. Results revealed that TTC values showed an approximate linear increase while the travel demand is increased up to 16%. After this value, TTC showed a sudden spike although the travel demand was linearly increased that means optimizing signal timings must be supported by applying psychical improvements.

Keywords

• Total travel cost,urban road networks,signal timing optimization,investment decisions

References

1. [1] Webster F. V., Traffic signal settings. Road Research Technical Paper, 39. HMSO, London, 1958.
2. [2] Allsop, R. E., Delay-minimizing settings for fixed-time traffic signals at a single road junction. Journal of the Institute of Mathematics and its Applications, 8, 164–185, 1971.
3. [3] Allsop, R. E., Estimating the traffic capacity of a signalized road junction. Transportation Research, 6, 245–255, 1972.
4. [4] Wong, S. C., Derivatives of performance index for the traffic model from TRANSYT. Transportation Research Part B, 29, 303–327, 1995.
5. [5] Heydecker, B. G., A decomposed approach for signal optimisation in road networks. Transportation Research Part B, 30, 2, 99–114, 1996.
6. [6] Wong, S. C., Group-based optimisation of signal timings using the TRANSYT traffic model. Transportation Research Part B, 30, 217–244, 1996.
7. [7] Wong, S. C., Group-based optimisation of signal timings using parallel computing. Transportation Research Part C, 5, 123–139, 1997.
8. [8] Wong, S. C., Wong, W. T., Leung, C. M., and Tong, C. O., Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control. Transportation Research Part B, 36, 4, 291–312, 2002.

9. [9] Girianna, M., and Benekohal, R. F., Application of genetic algorithms to generate optimum signal coordination for congested networks. Proceedings of the Seventh International Conference on Applications of Advanced Technology in Transportation, Cambridge, MA, United States, 762–769, 2002.
10. [10] Ceylan, H., and Bell, M. G. H., Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transportation Research Part B, 38, 4, 329–342, 2004.
11. [11] Ceylan, H., Developing combined genetic algorithm hill-climbing optimization method for area traffic control. Journal of Transportation Engineering, 132, 8, 663–671, 2006.
12. [12] Chen, J., and Xu, L., Road-junction traffic signal timing optimization by an adaptive particle swarm algorithm. 9th International Conference on Control, Automation, Robotics and Vision, Singapore, 1-7, 2006.
13. [13] Dan, C., and Xiaohong, G., Study on intelligent control of traffic signal of urban area and microscopic simulation. Proceedings of the Eighth International Conference of Chinese Logistics and Transportation Professionals, Logistics: The Emerging Frontiers of Transportation and Development in China, Chengdu, China, 4597–4604, 2008.
14. [14] Li, Z., Modeling arterial signal optimization with enhanced cell transmission formulations. Journal of Transportation Engineering, 13, 7, 445–454, 2011.
15. [15] Liu, Y., and Chang, G.-L., An arterial signal optimization model for intersections experiencing queue spillback and lane blockage. Transportation Research Part C, 19, 130–144, 2011.
16. [16] Ceylan, H., and Ceylan, H., A Hybrid Harmony Search and TRANSYT hill climbing algorithm for signalized stochastic equilibrium transportation networks. Transportation Research Part C, 25, 152–167, 2012.
17. [17] Dell’Orco, M., Baskan, O., and Marinelli, M., A Harmony Search algorithm approach for optimizing traffic signal timings. Promet Traffic & Transportation, 25, 4, 349–358, 2013.
18. [18] Dell’Orco, M., Baskan, O., and Marinelli, M., Artificial bee colony-based algorithm for optimising traffic signal timings. Soft Computing in Industrial Applications, Advances in Intelligent Systems and Computing, 223, Eds: Snášel, V., Krömer, P., Köppen, M., Schaefer, G., Springer, Berlin/Heidelberg, 327–337, 2014.
19. [19] Ozan, C., Baskan, O., Haldenbilen, S., and Ceylan, H., A Modified Reinforcement Learning Algorithm for Solving Coordinated Signalized Networks. Transportation Research Part C, 54, 40–55, 2015.
20. [20] Christofa, E., Ampountolas, K., and Skabardonis, A., Arterial traffic signal optimization: A person-based approach. Transportation Research Part C, 66, 27–47, 2016.
21. [21] Srivastava, S., and Sahana, S. K., Nested hybrid evolutionary model for traffic signal optimization. Applied Intelligence, 46, 113–123, 2017.
22. [22] Abdul Aziz, H. M., Zhu, F., and Ukkusuri, S. V., Learning based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility. Journal of Intelligent Transportation Systems, 22, 1, 40-52, 2018.
23. [23] Bell, M. G. H., Cassir, C., Grosso, S., and Clement, S. J., Path Flow Estimation in Traffic System Management. IFAC Proceedings Volumes, 30, 8, 1247-1252, 1997.
24. [24] Vincent, R. A., Mitchell, A. I., and Robertson, D. I., User guide to TRANSYT version 8. TRRL Report, LR888. Transport and Road Research Laboratory, Crowthorne, 1980.
25. [25] Ceylan, H., A genetic algorithm approach to the equilibrium network design problem. PhD Thesis, University of Newcastle upon Tyne, UK, 2002.
26. [26] Chiou, S-W., Optimisation of Area Traffic Control for Equilibrium Network Flows. PhD Thesis, University College London, 1998.
27. [27] Kimber, R. M., and Hollis, E. M., Traffic queues and delays at road junctions. TRRL report, LR909. Transportation and Road Research Laboratory, Crowthorne, 1979.
28. [28] Bell, M. G. H., and Iida, Y., Transportation Network Analysis. John Wiley and Sons, Chichester, UK, 1997.
29. [29] Storn, R., and Price, K., Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. ICSI, Technical Report, TR-95-012, USA, 1995.
30. [30] Allsop, R. E., and Charlesworth, J. A., Traffic in a signal-controlled road network: an example of different signal timings including different routings. Traffic Engineering Control, 18, 5, 262-264, 1977.
31. [31] Storn, R., and Price, K., Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11, 4, 341-359, 1997.