6 research outputs found
Modeling factors affecting utility to use internet taxi under Covid-19
No full textGiven the continuity of Covid-19, the urban transportation system has undergone remarkable changes. Besides, increased risks related to crowded places together with social distancing measures in public and shared transportation probably affect the usual choices of vehicles by passengers. In the present article, by using questionnaire design and online questionnaire in Tehran, attempts have been made to estimate the use of internet taxis by people during the pandemic. To this end, in order to specify the factors affecting the use of internet taxis, ordered and dual logit models were established using 233 data obtained from online inquiries and based on the amount of use and changes in using them before and after Covid-19. The results indicate that Covid-19 pandemic has had a negative effect on the use of internet taxis. Increasing the petrol prices and the lack of parking places at the destination have positively encouraged the use of internet taxis. Moreover, people who own a car use internet taxi less than those who do not. The number of these people not using internet taxis has been also reduced after the pandemic
Impact Of Congestion Pricing Policy Change On Mode Choice: The Case Of Tehran
No full textTransportation demand management policies are among the most important ways to reduce traffic congestion in cities and make transport infrastructures more efficient. One of the important policies in this field is congestion pricing that has been considered by various researchers to estimate and predict its effects including modal shift. In the present study, the effects of a new pricing policy on the traffic area of Tehran city, namely the acquisition of hourly basis tolls from personal vehicles entering this area, are studied. In this regard, the stated preference information was received through in-person interviews from 1588 users of this city-wide area who use personal vehicles for traffic in the area. In order to model their behavior in the face of the new pricing policy (hourly basis), multiple logit model was used. According to the results of the calibrated models, following the implementation of the 2000-Tomans hourly scenario, about 22% of the people entering the area by personal vehicles are going to shift their traveling mode to other modes including public (metro / bus), taxi, snap, and motorcycle. Of this, about 12% of people prefer the public transportation and will increase the share of this mode on daily trips. The Traffic Estimator's Elasticity Analysis showed that with a 1% increase in the average cost of the traffic plan in the utility function of the alternatives to change the way of travelling and other changes (cancellation of travel, change of destination to outside the range, and travel deferring to the weekend), the probability of choosing these alternatives increases by 0.77% and 0.61%, respectively. Furthermore, based on the analysis of the marginal effects of the traffic plan price variable, with the increase of 1,000 Tomans to the average cost of the traffic plan in the utility function of alternatives to change the way of travel and other changes, the probability of choosing these alternatives increases by 0.013 and 0.005, respectively
آثار مستقل و ترکیبی سیاستهای قیمتگذاری تراکم و بهبود سیستم اتوبوسرانی در استفاده از خودروی شخصی در سفرهای شغلی به محدودۀ زوج -فرد تهران
No full textToday, transportation demand management (TDM) policy tools are accepted as practical solutions for decreasing the costs of congestion in urban regions, and more efficient using of transport infrastructures. This paper investigates the role of a ``time-of-day congestion pricing scheme'' as a pull TDM policy and two push TDM policies including ``bus travel time reduction'' and ``bus
access time reduction'' in users' car use behavior. The main goal of this research is to estimate the impacts of these policy-tools on the probability of choosing car at morning peak, when they are applied separately or simultaneously.The analysis is based on the results of a stated preferences survey developed through the experimental design approach and was completed by 231 users, who travel into Tehran's even-odd zone for work by car. The advantage of data gathering in even-odd zone was that these commuters were familiar with the boundaries of pricing area and so, they could make a more realistic decision (for example, decision about choosing park-and-ride mode). For considering a time-of-day congestion pricing policy, we introduced a cordon pricing scheme from 6:30 AM with a discount on entering after peak period (in this case study, between 6:30 AM to 9 AM). Like other policies, the discount policy has three levels containing 50\%, 25\% and 0\% of peak period tolls.The independent and interaction effects of these policies are assessed by developing a two-level mode choice nested logit model and estimating marginal effects. This model has 8 alternatives, three of which are related to driving a car: drive before 6:30, drive between 6:30 and 9, and drive after 9 AM. Results show that cordon pricing scheme from 6:30 AM has the largest effect and could decrease share of drive between 6:30 and 9 AM by 0.408. Congestion charging scheme at 6:30-9 AM and bus access time reduction, are also the most effective policy-tools with a 0.49 decrease in car share when applied simultaneously
An application of stochastic user equilibrium assignment in the origin-destination matrix estimation
No full textEstimation (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
A HIERARCHICAL ANALYSIS OF FOOD COURT AND PARKING IMPACT ON TRAVEL TO SHOPPING CENTERS
No full textDestination choice problem is an essential element in transportation planning processes. The problem is to find the probability that a person traveling from a given origin will choose a destination among many available alternatives. In recent decades, applications of discrete choice models in trip distribution have increased. Destination choice models are coupled with several challenges, including large choice sets, complicated alternative specific attributes, and endogeneity problem. Determining the destination of trips with no fixed destinations, such as shopping and recreational trips (unlike mandatory trips), has been the focus of researches as soon as the activity/tour-based paradigms were introduced. Nonetheless, the classic destination choice models have paid less attention to psychological and personal attributes of travelers. Several studies on consumer behavior in shopping centers have revealed that in addition to observable emographic and socio- economic variables, latent constructs, such as psychological variables, lifestyle, and the orientation of the center, are important indicators to be considered to capture the true behavior of travelers. This paper presented a comprehensive analysis on shopping behavior of travelers in major shopping centers in Tehran, Iran. A hierarchical analysis of above-mentioned characteristics of costumers in choosing shopping centers with or without parking and food court was discussed. An internet-based survey was conducted to collect the required data for the modelling exercise which included information of 213 individuals. The nested logit model is currently the preferred extension to the simple multinomial logit discrete choice model. The appeal of the nested logit model is its ability to accommodate differential degrees of interdependence between subsets of alternatives in a choice set. The results did not reject the proposed hierarchical decision-making process hypothesis. While being aware of the biases associated with internet-based surveys, it was found that women and highly educated travelers prefer shopping centers with both parking and food courts, whereas people who travel by public transportation select centers with neither parking nor a food court facility
