هادی قلی

Estimation (correction) of origin-destination (OD) matrix based on traffic counts data is an inexpensive approach to predicting travel demand in transportation networks. The general formulation of this problem is a bi-level optimization program in which the matrix estimation is solved at the upper level, and the traffic assignment is solved at the lower level. In congested networks, deterministic user equilibrium (UE) assignment is often used at the lower level. Deterministic approaches assume that all users perceive network travel times the same way, which is not the case in reality. In contrast, stochastic methods allow for different user perceptions. This research develops the OD matrix estimation problem (ODMEP) under the stochastic user equilibrium (SUE) constraint. The SUE assignment with the multinomial logit (MNL) route choice model is applied at the lower level. The MNL model is a well-known discrete choice model with a straightforward, closed-form choice probability. Spiess gradient-based approach is used at the upper level, which is efficient in large-scale networks. The Spiess OD estimation models with UE/SUE constraints are implemented on the large-scale Tehran network under different user perception variances represented by the scale parameter (θ) in the MNL formula. Two scenarios are adapted to create the initial OD matrix to compare the results of the two models (ODMEP with UE/SUE assignment). Results show that ODMEP with SUE constraint outperforms ODMEP with UE constraint in producing link volumes close to observed traffic counts. Furthermore, the OD matrix resulting from the SUE-based model is better fitted to the real OD matrix than the UE-based model. However, the two methods' results converge when the scale parameter increases (i.e., variance in users' perceptions of network travel times decreases). In the Tehran network, the SUE-based model reduces the ratio of RMSE of the OD matrix to real demand more than 10 percent (more than 20 percent in some cases) compared to the UE-based model when the scale parameter is less than 0.5.

After applying the logit model as a route choice model in early stochastic traffic assignment algorithms, other models have been developed to overcome its theoretical weakness in considering path overlap. These models maintain the closed-form and computational convenience of logit, while not requiring simulation and computational efforts of probit. The purpose of this paper is to evaluate the most important of these models in comparison with each other. The models under consideration are Multinomial-logit (MNL) model, two modified MNL models called C-logit and PS-logit, and two models from the generalized extreme value family called cross-nested logit (CNL) and Paired-Combinatorial logit (PCL). These comparisons were performed at different levels of network congestion (origin-destination demand) and different values of perception variance of network users (scaling parameter) and were also compared with results of deterministic traffic assignment. Results of a nine-node and the Nguyen network show that as the congestion level increases, general patterns of link flows of stochastic assignments approach the deterministic assignment link flows; While the difference of these link flows in four models that consider the effect of path overlap increases compared to MNL model. Also, as the variance of user perceptions increases, the difference of link flows of stochastic assignments relative to the MNL model and relative to the deterministic assignment increases. Besides, results of the studied models for the Sioux-falls network show that path flows in stochastic assignments have much more entropy than the deterministic assignment, and therefore stochastic models produce more probable answers in this regard.

Improvement of public transportation systems through attention to service quality of these systems is important in order to increase the use of these systems. Various quantitative and qualitative attributes of travel are known effective on service quality of public transport systems. The investigated attributes in this research, with reference to regular bus system of the city of Tehran, are: in-vehicle time, fare, crowding and headway. For this purpose, the stated preference data of the respondents who travel to work by regular bus, collected. The survey was conducted during the last month of spring 2014 and 360 questionnaires were completed. Stated preference scenarios relating to aforementioned attributes were analyzed using multinomial logit models. Model results showed that while all the attributes have significant effect on the utility of bus, crowding is more important than the other attributes. In the other hand, similar to findings at developing countries, headway is much less important than the others. Furthermore, willingness to pay (WTP) values for improvement of investigated attributes were calculated and hourly value of time for commuters, was estimated 19,380 rials.