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Prior research on COVID-19 focused primarily on travel behavior changes before, during, and after the pandemic, with the aim of analyzing the significant variables. However, this research aims to study and compare the effects of traffic and transportation measures, specific events, and vaccination rates on the COVID-19 infection rate in Tehran, Iran. A correlation analysis is employed to investigate the degree of relationship between the number of infected individuals on each day and the implementation time of measures, events, and the vaccination rate. Over a 14-day period, the majority (67%) of measures and events had a significant impact on either decreasing or increasing the number of infections at a significant level of 1%. Results indicate that congestion pricing suspension has the most effect on decreasing the virus spread (correlation coefficient between -0.75 and -0.94). As another traffic-related measure, intercity travel bans also contributed to a decrease in infections. Additionally, certain holidays/events and their related movements and gatherings are linked to an increase in cases (correlation coefficient between 0.71 and 0.96). The ongoing decrease in infection rate could be attributed to the increasing vaccination rate, showing a negative correlation with a coefficient of -0.771. [ABSTRACT FROM AUTHOR]

Walking has been recognized as an important mode of transportation in recent years, and recent research has improved travel demand models for walk trips. One important added stage is the distribution of walk trips, which can be evaluated using destination choice models. Previous studies have overlooked the importance of origin trip attributes in the destination choice of walk trips. With the aim of improving destination choice models for discretionary home-based walk trips, a questionnaire based on the previous day's walk trips was used, and 422 trips were collected from individuals. A discrete choice logit model is used for discretionary trips by utilizing policy-related variables, such as origin-sensitive variables, land-use-related variables, and socio-economic conditions of individuals. Additionally, a solution is proposed to address the issue of data scarcity in considering the choice set. The results demonstrate that origin land-use (LU) variables, such as LU diversity index and access to green spaces, as well as socio-economic variables, like age and homeownership status, are statistically significant in the destination choice of discretionary home-based walk trips. One prominent result is that reducing the diversity of unattractive LU compared to increasing the diversity of attractive LU has a greater impact on the destination choice of such trips. Specifically, a 1% increase in the diversity of attractive LU in the origin area leads to a 0.031% increase in the probability of choosing a destination within that area, while a 1% decrease in the diversity of unattractive LU results in a 0.124% increase in the probability of choosing a destination within the area. The findings can be utilized in urban LU distribution and assessing their impact on destination choice for walk trips, ultimately informing future urban planning efforts in the context of pedestrian mobility. [ABSTRACT FROM AUTHOR]

Due to the potential of automated vehicles (AVs) to change the transportation system radically, it is essential to investigate the factors affecting users' intention to use them. Although previous studies have mostly focused on the latent variables of the internal schema of beliefs, this paper aims to examine the impact of internal and external factors by integrating the variables of the unified theory of acceptance and use of technology along with environmental concerns and perceived risk. Moreover, the effects of latent variables are also compared for different trip purposes (mandatory and optional), which have received less attention in previous studies. Using a stated-preference survey, 641 valid responses from citizens of Tehran, Iran have been collected. The estimation results of structural equation modelling show a significant difference between the determinants of AV acceptance across mandatory and optional trips. The estimated coefficients indicate that social influence and performance expectancy are the strongest explanatory factors in the intention to use AVs in optional and mandatory trips, respectively. However, no significant difference is observed for the impact of environmental concerns on the intention to use AVs across both trip types. [ABSTRACT FROM AUTHOR]

Fossil-fuel consumption in land freight is over 19%. Alternative fuel trucks (AFTs) help decrease greenhouse gas emissions. However, AFT preferences in international land transit have received little attention due to passing through various countries with different financial and regulation plans. This variety affects AFTs' market share. This study analyzes factors influencing AFT preferences in international land transit. A questionnaire (designed in four sections) was distributed among international transport companies in Iran and Europe. A principal component analysis helped extract principal components composed of cognitive, environmental, policy, practical, and economic variables. The multinomial logit models include 26 meaningful variables. The marginal effect analysis shows that the service quality of the manufacturer, importance of greenhouse gas mitigation, and social responsibility do not significantly elevate AFT preferences. In addition, cargo type influences AFT choice. The results of this study help to identify the barriers affecting AFTs' market share, which can positively impact air pollution. [ABSTRACT FROM AUTHOR]

Pricing and traffic rationing have emerged as effective and economically feasible strategies for mitigating traffic congestion in the central business districts of large cities. In Tehran, Iran's capital city, two separate surveys were conducted to evaluate the effects of time- and duration-based pricing strategies. The surveys included 1,388 participants from the congestion pricing zone (CBD) and 983 participants from the odd-even traffic rationing zone. The error component logit model was calibrated using stated preference (SP) scenarios for both the congestion pricing zone and the odd-even traffic zone; These models aimed to analyze modal shift, route choice, and time of travel in a day. Additionally, the mode choice behavior in the CBD was examined using the generalized mixed logit (GMXL) model, which was calibrated using both SPs and RPs. The findings indicate that implementing a duration-based scenario would result in a modal shift, changes in trip patterns, and even trip cancellations. On the other hand, the time-based scenario would primarily lead to changes in travel timing or destination choices. GMXL results show that duration-based pricing is more effective in shifting private vehicle trips to other modes. Furthermore, on-demand ride-hailing is less significant as a competitor mode within these zones. [ABSTRACT FROM AUTHOR]

The integration of shared mobility and autonomous vehicles (AVs) could potentially change the way parents decide to transport their children to and from school. A better understanding of the factors influencing parents' intentions to use shared autonomous vehicles (SAVs) for school transportation is necessary to enhance their children's mobility. Unlike prior research, this paper significantly contributes to the literature by exploring the impacts of socioeconomic, travel-related, and psychological factors and their interactions. Using Google Forms for an online survey, the authors collected 1435 valid responses from parents in Kerman city schools in Iran. The estimation results of the generalized ordered logit model indicate the significant impact of parents' socioeconomic status (occupation, education, income), travel behavior (accident experience, crash severity, travel cost), and attitude (innovativeness, perceived usefulness, environmental concern, pro-driving, safety), and their children-related factors (gender, the most frequently used travel mode, the possibility of tracking the child). The findings show that an increase in parents' education, perceived usefulness, and environmental concern increase the likelihood of their intentions to use SAVs. Finally, based on the findings, several implications are suggested to increase parents' intentions to use SAVs for transporting their children and to make SAVs a safe, affordable, and sustainable transport solution. [ABSTRACT FROM AUTHOR]

Sustainable and livable cities and non-motorized transportation modes are being increasingly promoted around the world. A considerable number of studies have investigated various factors associated with the severity of pedestrian crashes. However, little attention has been paid to the particular impacts of lighting conditions. This paper aims to differentiate between different lighting conditions based on solar altitude by developing separate Generalized Ordered Probit (GOP) models for the four distinct lighting conditions (i.e. daylight, twilight, dark, and lighted) analysing pedestrian injury severity at urban intersections. The models are tested using a set of log-likelihood ratio tests, which show there are key differences in the significance of the estimates, magnitude, and even the direction of the effects of explanatory variables, in which such differences may not be revealed by calibrating one aggregate model. Notably, built environments, age, gender of both pedestrian and driver, and speed limits were found to have different impacts on the severity of the crash. [ABSTRACT FROM AUTHOR]

This research integrates Unified Theory of Acceptance and Use of Technology (UTAUT) (Performance Expectancy [PE], Effort Expectancy [EE], and Social Influence) with Diffusion of Innovation Theory (TRialability [TR] and OBservability [OB]) as well as Perceived Risk (PR) to identify a wider set of latent factors affecting acceptance of fully automated AVs. Although research on AVs acceptance has been conducted in developed countries, it is a rather new topic in developing countries, where it has only been introduced as a promising technology to come. Structural equation modeling on stated preference surveys data of 641 Tehran residents in 2019 justifies the proposed integration of two theories and PR. Only PR shows an expected negative sign (−0.2); Among UTAUT variables, PE (0.33) and EE (0.25) were the most and least influential factors, respectively. Regression weights o DOI-related variables show that TR (0.17) and OB (0.16) have almost equal effect. [ABSTRACT FROM AUTHOR]

With the advent of autonomous vehicles (AVs), researchers have conducted various studies on the impacts of these vehicles, but limited research is found on the influence of AVs on private car ownership. Since AVs are not well-aligned with sustainable development, shared autonomous vehicles (SAVs) would be an appealing alternative. Hence, this paper aims to investigate the impact of socio-economic and travelrelated characteristics on private car ownership in the presence of SAVs among private car users in Tehran. After designing a web-based stated preference (SP) questionnaire and analyzing 2154 valid SP responses in 2022, more than a quarter (26%) of the observations are willing to reduce private car ownership level in presence of SAVs. Estimation results of binary logit model reveal that respondents aged 31-35 and 22-25 years old, as compared to other age categories, are more and less, respectively likely to sell their private cars. Further, due to the fact that users of SAVs, unlike private cars, do not need parking space, respondent are more likely to sell their private cars under this condition. Estimated coefficients of attributes considered in the SP scenarios indicate that increasing each of these attributes (travel time, waiting time, travel cost and number of passengers) in SAVs reduce the likelihood of selling a private car. Another important finding is the impact of respondents' experience with internet taxis (taxis ordered via an app on smartphones); those with frequent use or satisfy with this service, are more likely to reduce their car ownership. [ABSTRACT FROM AUTHOR]

The optimal location for establishing logistics centers is of great importance in reducing logistics costs and improving supply chain efficiency. This paper aims to provide a conceptual framework for finding the optimal location and capacity for a logistics village establishment using mixed-integer linear programming (MILP). The proposed model is applied on Qazvin province, Iran, as a developing country with a strategic location in international transport corridors. Unlike previous research, the proposed approach considers various logistics operations such as warehousing, refrigeration, sorting, and packaging, along with their capacities as distinct decision variables. The study area is divided into 6972 blocks of 1.5 × 1.5 km, of which 59% are infeasible and excluded due to environmental and natural hazard constraints. The MILP model is then applied in the GAMS for each feasible block to identify the best alternatives for the logistic village establishment with maximum total profit. Based on the results, total freight imported to Qazvin province is directly transferred to their final destinations without visiting the logistics village, while around 98% of exports of Qazvin province would first enter the logistics village to get a service before delivering to customers. [ABSTRACT FROM AUTHOR]

Technology advancements have paved the way for public access to shared autonomous vehicles (SAVs), but there is still no travel survey examining how SAVs with dynamic ride sharing (DRS) affect long-distance (LD) trips. Given the growth in these trips and the higher importance of travel time and cost on LD trips, assessing potential impacts of SAVs could be a vital tool in planning for a sustainable transportation system. This paper examines the impact of various attitudinal, sociodemographic, and travel-related characteristics on the usage intention of SAVs with DRS on LD trips. We have designed and conducted a web-based survey for this purpose and based on a representative sample of 723 individuals in 2021, a Generalized Ordered Logit model is estimated. Estimation results highlight the key importance of following psychological factors in a descending order: price evaluation, perceived usefulness, consumer innovativeness, sharing attitude, and privacy concern. Further, key factors among sociodemographic and travel-related characteristics are gender, education level, driving license, household car ownership, generational element, and crash history. These findings provide crucial insights into the likely effects of SAVs with DRS on LD trip behavior, based on which a number of practical implications are proposed for facilitating policy-making. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on the transportation sector, with changes expected to last for a long time. Following the reopening phases, increased use of private vehicles and continued avoidance of exposure to the infectious virus in public transportation exacerbated urban traffic and environmental problems. The paper aims to evaluate the impact of travel demand management strategies during the COVID-19 pandemic in Tehran, Iran. That city provided a unique case study because of its high COVID-19 contagion and death rates, traffic congestion, and lack of government restriction plans, which are common in developing countries. Four scenarios combining various travel demand management strategies were considered during the pandemic. The findings highlight the importance of considering the tradeoffs between different performance measures when selecting a suitable travel demand management strategy. Depending on the goals that decision-makers have set, the strategies may not be additive. While teleworking, e-learning, and rescheduling working hours all help reduce traffic congestion, the pricing strategy contributes significantly to meeting environmental and health goals. The findings also highlight the nonlinear pattern of change associated with each strategy. [ABSTRACT FROM AUTHOR]

The integration of automation and shared mobility services would significantly affect transportation demand, especially mode choice. However, little is known about how attitudes, travel attributes, and demographic factors affect the modal shift to shared autonomous vehicles (SAVs). A stated preference survey was designed to determine the preferences of car and transit users in relation to a modal shift to SAVs. The binary logit models' results revealed distinct behavior patterns and systematic heterogeneity among transit and private car users based on a representative sample of 607 individuals in 2021. The shifting behavior of both users is positively affected by attitudinal factors, including consumer innovativeness, perceived usefulness, sharing intention, and ecological awareness, while negatively affected by privacy concerns. In terms of travel-related attributes of SAVs, car users are eight times more sensitive to waiting times compared to transit users, who are three times more concerned with travel costs. Further, privacy concerns, the number of passengers sharing a trip, and the ratio of waiting time to travel time of SAVs were the major barriers to shifting the likelihood of car users' behavior. In light of these findings, based on the likely effects of SAVs on shifting behavior, a number of practical implications are suggested for more effective policy making. [ABSTRACT FROM AUTHOR]

Growing population and car dependency in developing countries have led to congestion that adversely affects the environment, travel time, trip cost, accidents, and public transportation reliability. Through implementation of travel demand management (TDM) policies, governments and policymakers aim to reduce private vehicle dependency and encourage people to use public transportation. Light rail transit is an important part of an attractive public transport. Rail transportation systems, though offer many potential benefits, are a major financial challenge for governments because of the high capital and operation costs. Therefore, passenger behavior must be determined before a new system is introduced. Using a stated preference (SP) questionnaire, private car users' behavior in Tehran's universities has been investigated to determine the explanatory factors affecting the modal shift to urban light rail transit (LRT). A binary logit model estimation results showed that men are less likely than women to shift toward LRT because they rely more on their private cars. It was the provision of free on-street parking at destinations and the frequency of using private cars on a weekly basis that had the most negative effect on LRT modal shift, demonstrating the critical role that implementation of TDM policies could play. Moreover, reduction in travel time by LRT has the most positive impact on modal shift toward LRT among private car users. As an interesting finding, marginal effect values indicate that a 10% reduction in travel time (0.32) has a greater impact than the possibility of sitting 50% of the travel time (0.25) on the likelihood of modal shift to LRT. Furthermore, the probability of modal shift to LRT will be reduced by 0.12 units for each unit increase in car ownership. In addition, owning a driver's license was also negatively correlated with LRT modal shift and decreased the likelihood by 0.27. This research will facilitate the decision-making and planning for future transportation systems to increase LRT's utility for potential users. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have a number of potential advantages, although some research indicates that this technology may increase dependence on private cars. An alternative approach to bringing such technology to market is through autonomous taxis (ATs) and buses, which can assist in making transportation more sustainable. This paper aims at examining the role of attitudinal, travel-related, and individual factors in preferences for a modal shift from conventional cars toward ATs and exclusive-lane autonomous buses (ELABs), exploring the existence of heterogeneity and its possible sources. The proposed mixed logit model with a decomposition of random coefficients uses 1251 valid responses from a stated preference survey distributed in Tehran, in 2019. Results show that there is significant taste variation among individuals with respect to ATs' travel costs, ELABs' travel times, and walking distances to ELAB stations. Furthermore, exploring the sources of heterogeneity indicates that women are more sensitive to ATs' travel costs and walking distances to ELAB stations while they are less sensitive to ELABs' travel times. Moreover, travel time in discretionary activities reduces the utility of ELABs more than it does in mandatory activities. Transportation authorities can use these findings to establish more effective policies for the successful implementation of AVs. [ABSTRACT FROM AUTHOR]

Increasing private car ownership and car dependency has led to a low share of walking as an active mode as well as congestion, air pollution, and health problems in developing countries. This paper aims to identify and compare the impacts of a selection of socio-economic, travel-related, and built environment variables on walking likelihood and respondents' sensitivity to the walking distance, both for discretionary and mandatory trips. The analysis drew its origin from 14,463 responses acquired through an extensive travel survey conducted in the city of Qazvin, Iran. The estimated binary logit coefficients show people's heterogeneity in the walking behavior for discretionary and mandatory trips. The results report a higher likelihood of walking on mandatory trips at almost all distances than the discretionary ones. Furthermore, investigating individual heterogeneity in different trip distances reveals that people aged less than 14 are more likely to choose walking on mandatory trips longer than 2400 m. Besides, those aged 25–44 years old or above 65 have less tendency to choose walking on mandatory trips with distances of 2000–2400 m and 800–1200 m, respectively. These findings are almost different on discretionary trips; compared to other age groups, people aged 15–24 years are less likely to choose walking on discretionary trips with a distance of 800–1200 m. Moreover, in trip distances of 1200–1600 m, the elderlies have a greater tendency to choose walking compared to other age groups. Some implications for more sustainable mobility in human-oriented urban environments are also presented and critically discussed. [ABSTRACT FROM AUTHOR]

Improving the service quality of public transport is necessary to maintain the loyalty of current users and attract new travelers. Past studies show that users' preferences for transit service quality are heterogeneous, but recognizing the factors describing this heterogeneity has not been investigated in depth. This paper aims to evaluate the sources of heterogeneity in passengers' preferences for service attributes of bus transit regarding the regular bus system in the city of Tehran. Four attributes of bus travel, including in-vehicle travel time, fare, vehicle crowding, and headway, were considered. Stated preference (SP) data of 360 commuters with work purpose was collected through face-to-face interviews. The random parameter logit (RPL) model, considering the heterogeneity around the mean of random coefficients, shows that heterogeneity can be explained to some extent by observed characteristics. It can be helpful for transit operators to adopt more efficient measures to improve service aspects. [ABSTRACT FROM AUTHOR]

To explain and predict active school travel (AST), most studies have not investigated to what extent considering taste heterogeneity is an important influence on AST share. The main aim of the present study was to evaluate whether considering unobserved taste heterogeneity through mixed logit models – including random coefficient and random coefficient analysis (RCA) – materially improves/influences the AST prediction compared to a simpler model – the multinomial logit (MNL) model. The database comprises 735 valid observations. The results show that, with a 10% increase in perceived walking time to school, the MNL model predicts that the AST share would decrease by 7.8% (from 18.9% to 17.4%) while the RCA model predicts that it would decrease by 8.5% (from 18.9% to 17.3%). Thus, the expected share of AST is overestimated by MNL by one-tenth of a percentage point. Although there might be random taste variations around perceived distance to school, it seems the other important policy-sensitive variables, such as safety perception, homogeneously impacts on the AST share across households with different socioeconomic and built environment characteristics. Our empirical assessment suggests that considering taste heterogeneity does not necessarily improve the accuracy of analysis for the aggregate share of the AST concerning policy-sensitive variables. [ABSTRACT FROM AUTHOR]

The origin-destination (OD) matrix, as a fundamental element of transportation planning, can be estimated in various ways. Owing to lower costs and the ability to keep matrices up to date, estimating OD matrices from observed traffic counts is gaining interest. These methods find an OD matrix that, when assigned to the network, reproduces the traffic counts. The main weakness of these methods, assuming traffic counts as deterministic values, has been overcome by the TFlowFuzzy (TFF) algorithm, which considers the counts as imprecise values. In this paper, a limitation of TFF is identified and accounted for with a methodological enhancement for modification. A key element in the estimation of trip matrices from traffic counts is the route choice proportions (assignment matrix). In the conventional TFF, the assignment matrix is computed only once and held constant during the whole estimation process. Hence, a modified algorithm is proposed in which the assignment matrix is updated successively. This modification is implemented on the real-size network of Mashad city. Results indicate a considerable improvement in the modified TFF goodness of fit compared to the traditional algorithm, as it is increased by 55%. This shows the effectiveness of the proposed algorithm, considering the assignment matrix endogenously. [ABSTRACT FROM AUTHOR]

Objective:Few studies have examined the differences in self-reported driver behaviour between drivers of one nationality who live in their country of origin (domestic drivers) and those who live abroad (expatriate drivers). This study aimed to explore the differences in self-reported driver behaviour among domestic and expatriate Iranian drivers. In addition, we explore the factors associated with self-reported accident involvement including personal injuries in these groups.Methods:A web-based version of the Driver Behaviour Questionnaire (DBQ) measuring self-reported driver errors and violations was distributed to respondents by availability sampling in social networks and by bulk email services. About 1000 individuals living in all provinces in Iran were invited to participate in the survey. We also invited Iranians living in more than 20 countries. A total of 634 domestic Iranians and 135 expatriate Iranians responded to our enquiry. The measure of driving behaviour asked the respondents about violations and error conduct during the last two years on any kind of road. The sample of domestic and expatriate drivers did not differ significantly in gender, age, education and driving hours per day. Domestic drivers were more likely to have experienced an accident in their lifetime. Both samples were relatively young of age (DomesticMinteger = 28.53, SD = 6.85; ExpatriateMinteger = 29.21, SD = 7.71).Results:The results suggested that emotional violations were more common among the drivers living in Iran. Emotional violations and driver errors were related to self-reported accident involvement among domestic drivers while ordinary rule violations were associated with such accidents in the expatriate group.Conclusions:Iranian expatriate drivers reported less emotional violations than domestic drivers. A potential reason is that road traffic infrastructure and regulation enforcement in high-income developed countries do not facilitate emotional violations. Implications for road traffic safety and methodological limitations of the study are discussed. [ABSTRACT FROM AUTHOR]

In land use modeling and common urban transportation, mostly the focus is on the aspects of land use effects on transportation patterns and less on the effects of urban transportation factors. For instance, the effects of road networks, any kinds of public and private transportation systems, the distance from the city center, traffic planned areas on urban land use such as residential, administrative, commercial and so on. This survey is to analyze and evaluate the effects of transportation accesses on urban land use patterns in 560 traffic areas of Tehran city. Using the statistical data of Tehran city, the effects of access on urban land use is analyzed by using the multiple regression model. Access and transportation as variables, in a row, have the most effects on residential, commercial, social, administrative, health, educational and recreational usages. It is also observed in Tehran city that the freeway and expressway networks, major and minor Arterial, collector and local streets, the middle rate of private car ownership are the most effective factors on residential usages and development or the scatter of these usages. In other words, in Tehran city, in the areas with the most shares in residential usages, the access to the public transportation is poor and this factor plays an important role expanding the use of personal vehicles in Tehran. Similarly, the ability of explaining and describing the changes of the commercial, social, administrative, health, educational and recreational land use by transportation variables reduces. [ABSTRACT FROM AUTHOR]

Time to collision is an important time-based safety indicator for detecting rear-end conflicts in traffic safety evaluations. A major weakness of the time to collision notion is the assumption of constant velocities during the course of an accident. In this paper we utilise equations of motion to develop a generalised formulation for time to collision by relaxing the assumption of constant velocity, constant acceleration and so on. This paper also illustrates how this concept can be applied to real-world data, and detailed data gathered in the NGSIM project are used. Then time to collision is calculated based on the assumption of constant speed, constant acceleration and linear acceleration for leading and following vehicles. Results indicate that in the third case (linear acceleration), the average duration of exposure to critical time to collision values is greater than the others. So, applying time to collision based on the assumption of linear acceleration in collision avoidance systems would decrease driver errors more than other cases. [ABSTRACT FROM AUTHOR]