Investigating acceptable level of travel demand before capacity enhancement for signalized Urban road networks Özgür BAŞKAN, Hüseyin CEYLAN, Cenk OZAN Teknik Dergi Technical Journal of Turkish Chamber of Civil Engineers, 2020 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.
A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks Ozgur Baskan, Huseyin Ceylan, Cenk Ozan Journal of Advanced Transportation, 2019 In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.
Improving the performance of the bilevel solution for the continuous network design problem Ozgur Baskan, Cenk Ozan, Mauro Dell’Orco, Mario Marinelli Promet Traffic and Transportation, 2018 For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.
Combined solution of capacity expansion and signal setting problems for signalized road networks Ozgur Baskan, Cenk Ozan Transportation Research Procedia, 2015 Traffic congestion has been growing at an unsustainable rate and decreasing the quality of life of people living in many countries especially in last few decades. At the same time, congestion causes decreasing accessibility and mobility whereas it leads to increase travel time and air pollution. Although various optimization techniques in determining signal timings or optimal capacity expansion have been discussed separately in the literature, few studies have been considered for solving the both problems simultaneously. Thus, it can be emphasized that the majority of literature fails to highlight an indispensable relationship between these two problems. To fill this gap, a bi-level solution methodology based on Differential Evolution (DE) algorithm is proposed in this study. The upper level deals with minimizing total system travel cost under given budget and signal timing plan while the User Equilibrium link flows are determined by VISUM at the lower level. In this study, the DE based solution algorithm is coded in VBA which is combined with VISUM for solving the problem. In order to illustrate the efficiency of the proposed algorithm, it is applied to real data of Sioux-Falls city network which has 76 links, 24 nodes and 552 OD trips. In this network, 7 nodes are considered as signalized junction, and 16 links which connect these nodes are chosen as candidate for capacity expansion. Results indicated that the proposed algorithm shows significant performance in solving the combined problem for signalized road networks.
