A Combined Model of Congestion Toll Pricing Based on System Optimization with Minimum Toll

Preventing the congestion is one of the main concerns of traffic managers and urban planners around the world. In response to this matter in transportation sector, planners have suggested toll pricing policies. This paper presents a combined optimization model as a new method to estimate the potential combination of travel time, and congestion toll that is implemented in a nine-node transportation network or Hearn network. This approach works as an urban travel demand management (UTDM) policy that imposes the cost of travel to travelers by calculating the marginal cost (MC), but the introduced model is optimized by minimizing the combination of travel time as an example of average cost (AC) and congestion toll as an example of MC simultaneously. Results show the total amount of flow is increased to 344,183 and the total amount of MC is decreased to 534,522 in comparison with the previous models

Novel Hybrid Method for Travel Pattern Recognition Based on Comparison of Origin-Destination Matrices in Terms of Structural Similarity

Origin-destination (OD) matrices provide transportation experts with comprehensive information on the number and distribution of trips. For comparing two OD matrices, it is vital to consider not only the numerical but also the structural differences, including trip distribution priorities and travel patterns in the study region. The mean structural similarity (MSSIM) index, geographical window-based structural similarity index (GSSI), and socioeconomic, land-use, and population structural similarity index (SLPSSI) have been developed for the structural comparison of OD matrices. These measures have undeniable drawbacks that fail to correctly detect differences in travel patterns, therefore, a novel measure is developed in this paper in which geographical, socioeconomic, land-use, and population characteristics are simultaneously considered in a structural similarity index named GSLPSSI for comparison of OD matrices. The proposed measure was evaluated using OD matrices of smartphone Global Positioning System (GPS) data in Tehran metropolitan. Also, the robustness of the proposed measure was verified using sensitivity analysis. GSLPSSI was found to have up to 21%, 15%, and 9% higher accuracy than MSSIM, GSSI, and SLPSSI, respectively, regarding structural similarity calculation. Furthermore, the proposed measure showed 7% higher accuracy than SLPSSI in the structural similarity index of two sparse OD matrices

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Quick Link Selection Method by Using Pricing Strategy Based on User Equilibrium for Implementing an Effective Urban Travel Demand Management

This paper presents a two-stage model of optimization as a quick method to choose the best potential links for implementing urban travel demand management (UTDM) strategy like road pricing. The model is optimized by minimizing the hidden cost of congestion based on user equilibrium (MHCCUE). It forecasts the exact amount of flows and tolls for links in user equilibrium condition to determine the hidden cost for each link to optimize the link selection based on the network congestion priority. The results show that not only the amount of total cost is decreased, but also the number of selected links for pricing is reduced as compared with the previous toll minimization methods. Moreover, as this model just uses the traffic assignment data for calculation, it could be considered as a quick and optimum solution for choosing the potential links.</p

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Improving SCATS Operation during Congestion Periods Using Internal/External Traffic Metering Strategy

Traffic metering is one of the effective strategies of preventing gridlock at urban signalized intersections during oversaturated conditions. This strategy could be implemented by adjusting signal timing schemes of the connected intersections of congested network in dynamic setting. This paper demonstrates the benefits of internal/external traffic metering strategy on a real case study in Tehran, the capital of Iran. For this purpose, the model outputs have been considered as input to set of SCATS scenarios for signal timing. In each test case the system is forced to use plans obtained from the model instead of using common built-in plans that had been used before, and the performance is measured using the VISSIM simulator to show differences. The results show significant improvement in network average travel time when using internal/external traffic metering strategy. Additionally, the average queue lengths are maintained near the optimal level since the model utilizes upstream arterial capacity

Congestion toll pricing and commercial land-use: clients' and vendors' perspective

This study outlines the effects of congestion toll pricing on commercial land-uses (CLUs) through studying the temporary and permanent impacts of client behavior on the CLUs. In the case study of Tehran metropolis, Iran's capital, which has experienced congestion pricing for more than four decades, both clients and vendors' viewpoints were modeled using discrete choice models. Two types of questionnaires were provided to evaluate clients' and vendors' behavior in response to the traffic congestion zone charges. The clients of three businesses, including garments, electronics, and home appliances, were more sensitive to toll price changes. A 20-percent increase in toll prices led to a substantial client loss in the above businesses in the long run due to accessibility decrease in their utility function. Consequently, the vendors preferred to change their approach and sell different goods; then, they gradually tended to migrate outside of the congestion zone

Residential development simulation based on learning by agent-based model

Increasing population and desire for urbanization increase housing demand in urban areas and ultimately induce growth and development of residential land-uses that result in urban sprawl. This paper simulates these sprawls of residential land-use in Qazvin city based on learning method by agent-based model. For this purpose, a model with the ability to learn from agents has been developed, in which families as agents can interact with each other and learn based on previous decisions. The model makes it possible to simulate residential land-use conversion based on the agent-based structure over the ten years by applying both demographic changes and household relocation desirability. The multiplication of the average level of land occupation by each family and the number of inserted new families indicates the potential magnitude of land-use changes. Also, results show the priority of residential development locations partially in the northeast regions and a small part of the south of Qazvin. These developments are expected to move towards the east in ten years

Comparing inequality in future urban transport modes by doughnut economy concept

Inequality is a problem facing the world community, especially in developing countries, that affects urban transport and vice versa. Which possible urban transportation mode will cause the least inequality? This is a vital question. The development of Autonomous vehicles (AV) has made Shared Autonomous Vehicles (SAV) one of the future transport modes. Active and public transport are also mentioned as applicable future modes, based on the literature. This paper aims to compare inequality in active transportation, public transport and SAV as the most important alternatives to private cars in the future. In this regard, we use doughnut economic concepts as the framework for our comparison. First, the inequality concept is expanded and then literature demonstrates the future desirability of modes. We show why doughnut economics could be a beneficial alternative for comparing that resulted in the superiority of active and public transport over SAV in terms of future inequality

Active transport network design based on transit-oriented development and complete street approach : finding the potential in Qazvin

Today, automobile dependency constantly causes traffic congestion, delays, reduced access, increased fuel and energy consumption, and environmental emissions. Automobile dependency has caused many direct and indirect transportation problems that may influence our life. Urban planners and transportation engineers seek to improve transport networks considering social issues. One of the most successful solutions for advocating sustainable transport is transit-oriented development (TOD). Another solution that planners encourage to use is designing the roadways based on a complete street approach, which is a system that provides safe, convenient, and comfortable travel and increases accessibility for users of all ages regardless of their transport modes. The present study employed the saturated roads that have heavy traffic most of the time and public transport e-ticket data to investigate the potential complete streets in Qazvin. An online questionnaire was developed using the Analytic Hierarchy Process (AHP) method based on the TOD and the complete street framework to investigate the essential criteria for redesigning the network based on the active transport approach. Thus, after analyzing the six criteria (density, diversity, distance, accessibility, demand management, and design) and eight sub-criteria (pedestrian flow, pedestrian density, connectivity, safety, bike route, bus route, road width, and urban tree canopy index), the proposed active transport network emerged and results show that five high priority streets were identified to be considered as a solution