Urban Transportation Modeling Research Articles

Analysis and development of criteria for the operational efficiency of urban electric buses

Problem. Currently, manufacturers and research specialists primarily utilize the amount of autonomous mileage and/or the specific electricity consumption of autonomous DEEs per unit of mileage to describe the general characteristics of urban electric buses. However, the structural mass and the key operational parameter, the nominal passenger capacity, are often disregarded. Therefore, comparing electric buses of the same type with identical dimensional parameters based solely on these criteria is illogical and incorrect. Goal. This study aims to establish criteria for the operational efficiency of urban low-floor electric buses of various types, considering autonomous DEE and their charging or replacement systems. These criteria should account for structural mass and nominal passenger capacity, facilitating a proper comparative assessment and the selection of the most optimal options for use in urban passenger transportation systems. Methodology. Criteria for the operational efficiency of city electric buses of various types are proposed based on an analysis of electricity consumption required for both movement and the operation of heating, ventilation, and air conditioning systems. The criteria consider the mass in the equipped state and the main parameter - nominal passenger capacity. Results. Several operational efficiency criteria for urban electric buses, incorporating mass parameters and passenger capacity, are suggested. The feasibility of their application is demonstrated through a comparison of five sketch projects of electric buses of various types with modern models. Originality. The proposed criteria for the operational efficiency of urban electric buses offer a method for adequately comparing any selected models, regardless of their types and technical specifications. Practical value. The proposed criteria for the operational efficiency of urban electric buses of various types can be beneficial for transport company specialists in selecting suitable rolling stock for specific routes. Additionally, design bureau and scientific and technical center specialists can utilize these criteria during the design process of new urban electric transport models.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

The role of traffic flow modeling in solving current problems of transport planning in Ukraine

The main current problems of the transport and planning infrastructure of Ukraine are identified. A brief description of traffic flows as an object of the street-road network is given including all off the factors that are affecting its functionality and the main reasons for the difficulty of studying them are listed and divided in. Modelling is proposed as one of the most effective tool in dealing with modern traffic problems with application of informational technologies. A brief history of the development of urban transport planning and modelling is given, highlighting its four most significant time periods with the description of notable changes during that time. Description of basic principles of transport modelling is structured and formalized. Four-stage computerized transport model is highlighted as the most common used and flexible, its basic assumptions and principles is mentioned. Various transport modeling techniques are proposed for consideration as one of the ways to aid in traffic flows managing. Definition of macromodelling, micromodelling, mesomodelling with its area of research, types of problems it is used to solve, basic advantages and disadvantages is given. The main software packages used today to solve various transport and planning tasks are listed, divided by the level of modelling it is capable to effectively perform. The main transport modelling software packages development companies is named with mentioning of its country-of-origin year of establishing and the most popular products. Along with that, a brief overview for the company's production is given taking into account their advantages and disadvantages and ability to be capable of working in different modelling levels at the same time. Conclusions are drawn summarizing the modern state of urban transport modelling in Ukraine with application of classical paradigm of traffic flow management and regarding further directions of research in this area for the most optimal use of modeling in issues of transport planning, such as creati ng of hybrid systems that make it possible to simultaneously study various characteristics of traffic flows at several levels of abstraction.

Research on the urban green transportation development level evaluation based on the triangular pythagorean fuzzy multiple attribute decision making

With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.

Traffic Modeling Using Machine Learning Methods for Predicting Vehicle Numbers at Junctions: A Case Study in Colombo, Sri Lanka

Traffic modeling is a fundamental tool for comprehending the complex dynamics of urban mobility. It provides vital insights for transportation planning and the construction of infrastructure. This study aims to employ sophisticated machinelearning techniques to forecast the volume of vehicles at certain main intersections in Colombo, Sri Lanka. The study emphasizes the importance of traffic modeling in influencing policy development and optimizing transportation networks, based on a thorough evaluation of relevant literature. The research utilizes machine learning methods, namely random forest regression, to analyze temporal and spatial trends in traffic flows. This analysis yields valuable insights for urban planners and transportation authorities. An examination of the dataset, utilizing methodologies such as rolling statistics and time series analysis, reveals subtle variations in traffic levels over time, including the influence of external influences like as the COVID-19 pandemic. The study's results highlight the significant impact of incorporating machine learning techniques into traffic modeling, providing a solution to improve urban mobility, decrease congestion, and create more sustainable transportation systems. This research ultimately enhances the development of data-driven solutions to tackle the changing mobility requirements of Colombo and establishes the groundwork for future research in the field of urban transportation modeling

An open dataset on individual perceptions of transport policies

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households’ demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual’s perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.

Environmental and economic evaluation of a low emission zone for urban freight transport

This paper aims to quantify the environmental and economic impacts of a low emission zone in the context of urban freight transport. The research's originality lies in its combination of behavioral data, urban freight transport and environmental models, and cost-benefit analysis methodologies. Through this methodological framework, we examine the costs and benefits of implementing a low emission zone using different scenarios with varying perimeters and environmental requirements in the urban area of Lyon. The findings of this study suggest that increasing the size of the perimeter is more economically and environmentally efficient than tightening the level of environmental standards.

Optimization Model of Urban Rail Transportation Planning Based on Evolutionary Algorithm of State Space Model

Optimization Model of Urban Rail Transportation Planning Based on Evolutionary Algorithm of State Space Model

Sample Survey of Passenger Traffic by Analysing Wi-Fi Data in Moscow Transport Hub. Part 2

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks. For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant. This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly. The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool. The study has revealed that, on average, up to 40 % of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices. The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. This issue contains the second part of the article.

Sample Survey of Passenger Traffic by Analysing Wi-Fi Data in Moscow Transport Hub. Part 1

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks.For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant.This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly.The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool.The study has revealed that, on average, up to 40% of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices.The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. You can find the first part of the article in the issue.

Combination model of urban tourism transportation based on nested logit model

ABSTRACT The traditional combination model based on multinomial logit is incapable of reflecting the nested structure of urban tourism destinations. Therefore, this paper established a variational inequality model of the combination of tourism demand distribution and transportation assignment based on nested logit. Through the first-order optimization conditions, it proved that the volume of travel distribution and transport assignment could meet the combination equilibrium conditions. Based on the MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified tourism passenger transport network in Huangshan City. The calculation results show that the variational inequality model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit, the nested logit structure fully considers the attraction measure of hotel and scenic spots, which is more in line with the choice behavior of tourists when choosing transportation route. In addition, the time value type of tourists has a great impact on the comprehensive tourism impedance which means that in the practical application, it is necessary to determine the time value type of tourists in each transportation routinereasonably.

Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions

Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation.

Development Model of Urban Ecological Transportation An Analysis Based on Beijing

Transportation is a kind of derived demand from social and economic activities. The transportation demand has been increasing with the rapid development of China's economy. Meanwhile it has resulted in some negative problems, especially in cities. Based on fully understanding of the connotation and theoretical foundation of urban transportation and ecological transportation, this paper summarizes the typical urban ecological transportation development mode, analyzes the mode selection and development status of Beijing and finally puts forward some relevant development suggestions.

Modelling the urban freight flows for impact assessment of the urban consolidation centres by using the origin-destination matrices

The urban consolidation centre (UCC) is the most popular city logistics (CL) initiative for both, researchers and practitioners. Urban freight transport (UFT) models are often used to assess the impact of the UCC on urban freight flows. A number of UFT models are based on the origin-destination (O-D) matrices for freight distribution. However, the static nature of O-D matrices imposes the limitations to the O-D based models regarding the CL initiatives impact assessment. We propose a new way of aggregated urban freight flows modelling, whereby the O-D matrices are transformed depending on the number, location and the share of freight flows attracted by the introduced UCCs. To exemplify the idea, the model is applied in the case of Novi Sad, whereby three different UCC locations and seven distribution patterns were compared. The results might be of interest for the researchers interested in the CL initiatives impact assessment and practitioners involved in the UFT decision-making process.

A heuristic technique for traffic assignment with variable step size and number of iterations

The prediction of traffic flows on network links is an essential issue in urban transportation modeling and planning. Although manual assignment methods are possible for tiny networks, the networks involved in practical-size problems usually require complex calculations with large amounts of data. In the traditional incremental traffic assignment technique (TITAT), the O-D matrix is divided into N equal portions and assigned to the network at each iteration. Travel times are then updated, based on which the next portion of the O-D matrix is loaded onto the network. In this paper, the assumption of equal step size in all iterations is relaxed, and variable step size for the different iterations is proposed to increase the efficiency of this technique. It is shown that an intelligent choice of step size can cause more efficiency. To implement this concept quantitatively for a real-size problem, Mashhad, as the second-largest city of Iran with about 4.5 million trips per year, is used as the case study to analyze and evaluate the proposed technique and compare the results with those of TITAT. Results suggest that regarding the reliability of the outcomes and computational efficacy, the proposed algorithm is as good as other methods. Unlike other methods, there is no additional parameter to be calibrated, and the convergence behavior of the algorithm is promising. It is observed in this particular case, that there is an inverse linear relationship between the number of iterations and the initial step size; and that for a specified number of iterations, the best results are obtained for equal step sizes for all but the first iteration. The main advantage of this technique is that it can provide a simple and economical method for traffic assignment as compared with user equilibrium for which goodness-of-fit measure of predicted-observed flows on selected links equals 0.57.In contrast, this measure for the proposed incremental traffic assignment technique (PITAT) equals 0.52. The main shortcoming of the method is the fact that it is a heuristic method. Despite showing promising results, the convergence of the algorithm has yet to be practically proved. Considering the order of calculations, PITAT seems very attractive and can be applied as a substitute in situations similar to the case of this paper.

RsRS: Ridesharing Recommendation System Based on Social Networks to Improve the User’s QoE

Nowadays, one of the most outstanding new urban transport model is the ridesharing service, in which two or more users share a ride. This transport model reduces costs and the number of circulating vehicles, improving user mobility. In the ridesharing service, the users’ quality is a tangible novel evaluation parameter. Consequently, this study treats the use of quality of experience (QoE) in the ridesharing service context, proposing a recommendation system (RS) for ridesharing services (RsRS), which considers user profile information extracted from online social networks (OSN) and user preferences. Thus, the main objective of the proposed RsRS is to improve users’ QoE. To this end, the users’ profile for the ridesharing service is built based on OSN data, which includes group of users with similar characteristics in the same trip, thus avoiding users with opposite preferences. First, subjective tests are carried out to obtain information on users’ preferences and the results are analyzed via machine learning algorithms to determine the various user profiles. The experimental results demonstrate that the random forest algorithm has the best performance, considering OSN data and explicit preferences saved in the proposed solution and only OSN data, for average F-measures of 0.92 and 0.91, respectively. Additionally, a ranking containing a list of recommended users to share a ride is determined using a similarity function, and the results demonstrate that 94.2% of assessors agree with the proposed recommendations. Furthermore, the RsRS has a modular configuration and its integration with a real ridesharing service providers is also discussed.

Modeling Household Vehicle Ownership in Emerging Economies

Household vehicle-ownership model is a critical part of urban transportation modeling system. This paper offers a comprehensive review on household vehicle demand models at disaggregate level, which consists of four aspects: data, methodology, application and prospect. The first section makes a relevant review on data source and type, and introduces the application of panel data and RP/SP data. In the methodology section, various modeling approaches for vehicle ownership are summarized into two broad categories, including static and dynamic models. Based on research objectives, vehicle-ownership models can be applied to forecast household vehicle count, vehicle type, vehicle use and vehicle transaction. Furthermore, the explanatory factors used in models are listed, and model applications are reviewed for emerging economies and particularly in the context of developing countries. Lastly, the prospect on the challenges and opportunities are discussed in the final section to provide references for future research.

The reduction of the fuel consumption and of the pollution through the city public transport

The public transportation represents the ideal solution that can be offered with the purpose of satisfying the mobility request through an urban area. A highly organized and efficient public transportation system which offers adequate movement possibilities, in a short time and through high safety and comfort conditions, forms the basis of an urban transportation system that improves the sustainable development of a urban area. Over this paperwork it is going to be estimated the optimization effects of the Pitesti urban transportation system, and even more, it is going to be established the „translation” way of the different improvements for the public transportation system as input data into the urban transport model. In this way it will be shown how the renewal of the vehicles and the modernization of the bus stations could get to the increasing of the urban transport share, thus succeeding the decrease of the own vehicles transport. As a consequence, through the private vehicles movements decrease it is get, in the end, a fuel consumption and gas emissions reduction, in the same time resulting a high traffic performance both for the public transportation and for the own private vehicle transportation

Study on reduction of the fuel consumption and the pollution by using traffic management systems in urban areas

The traffic management systems are implemented on a large scale during the last decades in urban areas due to their feature to optimize the traffic capacity, in a simultaneous and real time, not only at the individual junctions but also the entire city level. This is achieved through the short term predictions, using the traffic data bases collected by the specific sensors from the traffic controlled crossroads or from other places into the city street network, data that’s used on-line in order to optimize the traffic lights cycles onto all the controlled crossroads. In this way it is accomplished a optimal capacity for a certain traffic volume over a period of time. Using this method good results are obtained considering the reduction of the fuel consumption and of the pollution level. Another result is the increasing of the average speed of vehicles and the decreasing waiting times on the crossroads, which is beneficial for both cars and public transport. The case study for the Pitesti city highlights these aspects and shows how the urban transport model could be used for quantifying the effects of implementing the traffic management systems. The paperwork summary regarding the public transportation is attached.

Post-earthquake reconstruction as an opportunity for a sustainable reorganisation of transport and urban structure

The reconstruction of a damaged historic/modern urban system following a natural catastrophic event, even if sophisticated, complex and expensive can become an opportunity to apply innovative methods of urban regeneration oriented towards environmental sustainability. A research by the University of L'Aquila exploring the consequences of the 2009 earthquake in central Italy, both in analytical and design terms, is focused on this theme, applying innovative principles to the reconstruction project of the city of L'Aquila, capital of the Abruzzo Region (Italy). The study carries out an analysis of the earthquake effects on urban systems and mobility and proposes an operating strategy based on a multidisciplinary approach that integrates urban design and sustainable mobility strategies. In particular, it outlines the components of Strategic Projects that has been developed for the reconnection of the urban fragments created by the dispersion of the city after the earthquake, which has applied the principles of multi-modal sustainable mobility.The results of the study show that the post-quake reconstruction, if properly interpreted, can be a unique opportunity to conceive and construct the conditions to implement a new and more efficient urban sustainable transport model through advanced technologies based on outlining an innovative urban redesign approach.