Urban Transportation Management Research Articles

A two-stage stochastic programming approach for dynamic OD estimation using LBSN data

Estimating origin–destination (OD) demand is essential for urban transport management and traffic control systems. With the ubiquity of smartphones, location based social networks (LBSN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. Its nature of confirmed trip purpose (activity) and activity chain host makes it more advantageous than other data (e.g., household travel surveys and traffic network detection). On the other hand, LBSN data is a more direct and accurate representation of demand patterns and can remove the significant burden of developing traffic models and estimating simulation-based objective functions. However, thus far, most LBSN-based estimation models only focus on static (day-level) OD estimation, making less use of those characteristics. To this end, this paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level dynamic mobility flows using LBSN data. The first-stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second-stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. Markov chain Monte Carlo (MCMC) sampling is used to generate plausible check-in scenarios. A generalized Benders decomposition (GBD) algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments on the case study of Tokyo, Japan, under the employment of the generalized least squares (GLS) estimator. The results show that algorithm convergence can be guaranteed within several iterations. The approach can provide satisfactory estimations of check-in patterns, zonal production and attraction, and OD flows. Furthermore, multiple objective function states are tested for evaluating the completeness of the proposed framework and exploring its potential for simplification and extension. Incorporating specific penalty terms into the objective function also provides a way to verify the reliability of the two-stage structure and validate the effectiveness of the model. Finally, we discuss the model enhancement from the perspectives of online OD estimation by integrating with LBSN simulations, network-wide OD extrapolation using appropriate scaling methods, and removing the user-side data requirement by leveraging activity chain modeling. The proposed framework provides a novel and effective approach to OD demand estimation using LBSN data.

Study of Minimum Service Standard Index in Public Transportation: Sample of Trans Pakuan Bogor

Public transportation in Bogor City is one of the issues that is still a concern and needs to be studied. Urban transportation management involves many strategic aspects and issues, such as reducing gas emissions, multimodal integration, and increasing public awareness and convenience to change the habits for using mass transportation. Buy the service program from BPTJ Ministry of Transportation through Trans Pakuan has been operating since November 2021 and during the operational period studies are still needed to achieve and improve service standards. This study uses field survey methods and quantitative analysis of the 8 dimensions of minimum service standards for mass transportation. The result shows that the Public Perception Service Index (Trans-Pakuan) shows "good" conditions in each corridor. However, the Trans Pakuan operation is considered ineffective because the loading factor is dominated by students, not workers. There are still 61.6% distance from bus stops above 1 kilometer with travel time to the bus stop taking more than 10 minutes by 56.9%. Trans-Pakuan BTS users are expected to be shifted to communities with a higher economic level by conducting in-depth studies to find out the expectations of public transport standards desired by middle income to higher income.

Exploring Modal Choices for Sustainable Urban Mobility: Insights from the Porto Metropolitan Area in Portugal

Efficient and sustainable urban mobility is critical for contemporary cities, and understanding the factors influencing modal choices is essential for addressing transportation challenges in metropolitan areas. This study focuses on the Porto Metropolitan Area (AMP) in Portugal and aims to gain insights into these factors. Using data from the last mobility survey (IMob) conducted in 2017, a multinomial logit (MNL) model is used to analyze individual modal choices amongst private motorized vehicles (PMVs), public transport (PT), and active modes (AMs). The findings unveiled that demographic, socioeconomic, and travel-related characteristics substantially influence individual modal choices within the studied area. Moreover, probability scenarios highlight the importance of financial considerations, environmental consciousness, and accessibility to public transport in promoting sustainable transportation options. These insights have significant implications for policymakers and stakeholders involved in urban planning and transportation management. This study contributes to the literature by providing valuable insights into individuals’ transportation preferences and behaviors, facilitating decision-making based on evidence for infrastructure improvements and targeted interventions. By promoting sustainable transportation alternatives and reducing reliance on PMVs, this study aims to enhance the livability and sustainability of the AMP, aligning with long-term sustainability goals.

Estimating intercity heavy truck mobility flows using the deep gravity framework

Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban planning, transportation management and logistics operations. The inaccessibility of big data related to intercity transport systems and the heterogeneity of trucking activities pose challenges for the reliable estimation. Recently, the advance of Artificial Intelligence (AI) provides a potential solution to this problem. However, most previous studies focused on the estimation of inter-regional passenger mobility. In-depth studies of estimating intercity heavy truck mobility flows by using deep learning techniques are still scarce. To fill in the gaps, we construct a deep neural network based on the Deep Gravity framework, an advanced predictive model for human mobility. We collect a wide range of data related to heavy truck movements, freight locations, road networks and land uses to train the model, and validate its high performance by comparing to traditional gravity model. Furthermore, we use an explainable AI technique to interpret how the city features contribute to the determination of intercity heavy truck movements, and the results can provide valuable policy implications for logistics operations, businesses and urban planning.

Traffic Congestion Detection and Alternative Route Provision Using Machine Learning and IoT-Based Surveillance

The Automated Dynamic Traffic Assignment (ADTA) system introduces a novel approach to urban traffic management, merging the power of IoT with machine learning. This research assessed the system's performance in comparison to traditional traffic management strategies across various real-world scenarios. Findings consistently showcased the ADTA's superior efficiency: during peak traffic, it reduced vehicle wait times by half, and in scenarios with unexpected road closures, congestion detection was almost five times quicker, rerouting traffic with a remarkable 95% efficiency. The system's adaptability was further highlighted during weather challenges, ensuring safer vehicle speeds and substantially reducing weather-induced incidents. Large-scale public events, known disruptors of traffic flow, witnessed significantly reduced backlogs under the ADTA. Moreover, emergency situations benefitted from the system's rapid response, ensuring minimal delays for critical vehicles. This research underscores the potential of the ADTA system as a transformative solution for urban traffic woes, emphasizing its scalability and real-world applicability. With its integration of innovative technology and adaptive mechanisms, the ADTA offers a blueprint for the future of intelligent urban transport management.

A Method of User Travel Mode Recognition Based on Convolutional Neural Network and Cell Phone Signaling Data

As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application.

The Role of Decision Optimization in Urban Transport Management

The Role of Decision Optimization in Urban Transport Management

Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data

ABSTRACT The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.

Exploring key spatio-temporal features of crash risk hot spots on urban road network: A machine learning approach

Traffic safety is a critical factor that has always been considered in policy making for urban transportation planning and management. Accurately predicting crash risk hot spots allows urban transportation agencies to better implement countermeasures towards enhancing traffic safety. Considerable efforts have been devoted to investigate crash risk hot spots in many previous studies. We hereby identify three research gaps that remain to be resolved: first, the effects of spatio-temporal features surrounding hot spots are often ignored; second, false discovery rates tend to be higher when applying local spatial indices to identify hot spots; and third, the spatio-temporal correlations and heterogeneity of crash-related features in a subject spot and its neighboring spots have not been well captured in most crash risk prediction models. To fill these gaps, we propose an urban crash risk identification model by integrating space-time cubes and machine learning techniques. The spatio-temporal correlations and heterogeneity of crash-related features are represented by using statistical descriptions of neighboring cubes. Three space-time cube risk datasets collected from Manhattan in New York City are used to validate the proposed model in the case study. The eXtreme Gradient Boosting (XGBoost) classifier is employed to predict the risk patterns (hot spots, normal spots, and cold spots) of each cube due to its satisfactory prediction performance. The validation results suggest that our proposed model attains lower false discovery rates and higher crash risk prediction accuracy as compared to conventional methods. As the results of the feature selection are empowered by machine learning, we found that most key features are inherent to the features of spatial neighboring cubes, which manifests the importance of considering the features of neighboring spots when identifying crash risk hot spots. Moreover, SHapley Additive exPlanations (SHAP) are employed to interpret the effects of key features on hot spots, upon which the contributions of the features related to urban facilities, public transit, and land use are discussed. Based on the feature interpretation, several policy recommendations could be made to enhance urban road traffic safety in the future.

Разработка информационного обеспечения для интеллектуального управления городскими рельсовыми транспортными системами

The article discusses generalized approaches used in designing information support for intelligent transportation systems, based on the example of a train dispatcher simulator and an automated system for building schedules of passenger trains movement in the Moscow Metro as interrelated systems within a unified technological process of organizing the movement of rolling stock along the lines of the Moscow metro, Moscow Central Circle, Moscow Central Diameters, and the city’s tram network. The article analyzes the existing systems developed by the team of the “Management and Protection of Information” department at the Russian University of Transport (MIIT) for the State Unitary Enterprise “Moscow Metro”, an integral part of which are information systems. Common properties of the systems are identified for information support, and a classification of tables serving as the basis for implementing urban rail transport management systems is proposed. The article lists the properties that the designed information support of the intelligent transport system must possess, and proposes a methodology for its construction. The article presents the structures and functional features of information systems used for building an intelligent transportation system, including the content of databases that serve as a link between the upper interface and lower technical levels. The selection of the database management system is also justified.

Enhancing Artificial Intelligence in Advanced Database Systems for Baghdad's Urban Transportation Management

Enhancing Artificial Intelligence in Advanced Database Systems for Baghdad's Urban Transportation Management

Future urban transport management

The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector. However, existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems. Furthermore, emerging modes in urban mobility have not been sufficiently studied. The National Natural Science Foundation of China (NSFC) officially approved the Basic Science Center project titled “Future Urban Transport Management” in 2022. The project members include leading scientists and engineers from Beijing Jiaotong University, Beihang University, and Beijing Transport Institute. Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities, this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management. It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.

How do taxi usage patterns vary and why? A dynamic spatiotemporal analysis in Beijing

Existing studies lack attention to taxi usage dynamics, considering its trip proportion over other travel modes and its influencing factors at fine spatiotemporal resolutions. To fill these gaps, we propose a method for examining taxi usage in a grid of 1 km × 1 km cells per hour during a one-day cycle in Beijing. This method measures the differences between taxi trips from taxi trajectory data and mobile signaling data in the same week in January 2017. To explain the spatiotemporal variation in taxi usage, multiple linear models were used to investigate taxi usage dynamics with alternative transport modes, socioeconomic factors, and built environments. In summary, this study proposes to develop an indicator to measure taxi usage using multiple data sources. We confirm that taxi usage dynamics exist in both temporal and spatial dimensions. In addition, the effects of taxi usage factors vary over each hour in a one-day cycle. These findings are useful for urban planning and transport management, in which the dynamic interactions between taxi demand and distribution of facilities should be included.

Population mobility and urban transport management: perspectives environmental quality degradation and sustainable development of suburban Makassar City, Indonesia

The expansion of the area towards suburban areas accompanied by suburbanization has an impact on the conversion of productive agricultural land and transportation movement systems. This study aims to analyse land use change works as a determinant of environmental degradation in suburban areas, the effect of land use changes, socio-economic activities, population mobility and transportation systems on environmental quality degradation, and models for handling land use, population mobility, transportation infrastructure and system management transportation towards sustainable development of suburban areas. This study uses a sequential qualitative-quantitative approach. Data obtained through observation, survey and documentation. The results of the study show that the intensity of land use change coupled with population mobility, in addition to affecting the urban transportation system based on the pattern of origin and destination of travel, also has an impact on the environmental quality degradation of suburban areas. Changes in land use, socioeconomic activities, population mobility and transportation systems has an effect in the environmental quality degradation of suburban areas with a coefficient of determination of 95.65 percent. This study recommends the application of a land use management model, population mobility, and transportation infrastructure towards the sustainability of the suburbs of Makassar City, Indonesia.

THEORETICAL FOUNDATIONS OF URBAN TRANSPORT AND LOGISTICS SYSTEMS INTELLECTUALIZATION

Topicality. The scientific article is devoted to a research analysis of the features of intelligent technologies use for logistics processes managing, their impact on digital transformation and modernization of logistics systems. Intellectualization is a new trend in the economic development of countries derived from smart technologies, including cloud computing, mass data and the Internet. Intelligent program services based on intelligent terminals, provided by Internet enterprises, bring an unprecedented effect and challenge in this field. The relevance of the study derived from the digital transformation both the world economy and Ukraine, in particular, and within these conditions the new information technologies and intelligent systems are being formed and applied to improve and modernize transport and logistics processes. The article deals with theoretical aspects of the logistics processes intellectualization.Aim and tasks. The purpose of the work is to study the theoretical and methodological foundations for the implementation of the intellectualization concept for the management of urban transport and logistics systems in terms of adaptability.Materials and methods. The work used the methods of theoretical research, namely: the method of analysis, synthesis, the method of going from the abstract to the concrete, deduction and induction, and the method of idealization was also used in the article.Research results. During the study it was found that the features of transport and logistics systems depend on the development strategies of administrative-territorial units and the growth driver. The best countries of the world were determined by the criterion of logistics efficiency, and that made it possible to compare the results of these countries with Ukrainian indexes. Based on the results of a comparative analysis, it is substantiated that the strong position of European countries in the ranking is due to the efficient operation of their transport system and focus on the use of achievements in the field of intellectual technologies, in contrast to the weak position of Ukraine, that is due to the insufficient use of adaptive information and communication technologies in organizing of complex supply chains by subjects of transport and logistics infrastructure. The functional components and objects of intelligent transport systems are characterized. It has been established that autonomous intelligent transport systems are mainly used, which are not integrated, that, in turn, reduces their effectiveness. A theoretical and applied model for the development of urban transport and logistics systems in conditions of adaptability is built, which is based on the modern empirical and theoretical basis of the development of urban transport and logistics systems, which are influenced by the processes of globalization, socialization and intellectualization involving the use of computer, information and communication technologies when controlling vehicles and transport networks in real time. The key aspects of influence on the management of the transport system in the conditions of its socialization and intellectualization are indicated. The necessity of strategic plans and programs developing to implement the effective use of intelligent transport systems applied technologies is substantiated, that will contribute to solve the transport problems of the cities. This made it possible to determine the main conceptual foundations for the adaptation of intelligent transport technologies to the urban logistics system, which is currently in the conditions of locality, heterogeneity, with a low level of adaptability and orderliness.Conclusion. The rapid development of modern cities, the increase in the number of vehicles, the development of new routes increases the amount of information needed for analysis and promt decision-making, and this, in turn, stimulates the active use of intelligent information technologies. Currently, a new stage of logistics is developing, which manifests itself through the socialization and intellectualization of logistics activities, that involves a continuous process of knowledge obtaining, its increasing, transforming and processing to find an effective solution in the system managing at a given time. Therefore, the further development of urban transport should be based on the use of computer technology, and namely intelligent transport systems. It is expedient for cities and other territorial-administrative units to develop their own approaches corresponding to their own unique characteristics in the development of information and transport systems.

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

Visualizing population mobility from spatiotemporally aggregated mobile phone data via a 3D gradient approach

AbstractPopulation mobility patterns are important for understanding a city's rhythms. With the widespread use of mobile phones, population‐based trajectories can be utilized to explore such mobility patterns. However, to protect personal privacy, mobile phone data must be de‐identified by data aggregation within each spatiotemporal unit. In data acquired from mobile phones, population mobility features are still implicit in the spatiotemporally aggregated grid data. In this study, based on image‐processing techniques, a two‐step 3D gradient method is adopted to extract the movement features. The first step is to estimate the initial movement pattern in each spatiotemporal grid, and then to estimate the accumulated movement pattern within a time period around a geographical grid. This method can be applied adaptively to multi‐scale spatiotemporal grid data. Using geospatial visualization methods, estimated motion characteristics such as velocity and flow direction can be made intuitive and integrated with other multiscale geospatial data. Furthermore, the correlation between the population mobility pattern and demographic characteristics, such as gender and age groups, can be analyzed with intuitive visualization. The implication of the visualization results can be used for understanding the human dynamics in a city, which can be beneficial for urban planning, transportation management, and socioeconomic development.

RAILDASH: A DASHBOARD SYSTEM TO ANALYZE EFFECTS OF EVENTS ON RAILWAY TRAFFIC USING BIG GPS DATA

Abstract. The big events cause substantial deviations in the city traffic, especially railway transportation which happens to be the dominant mode of transportation for most of the major cities. Analysis of big events’ impact on railway transportation, therefore, is of great importance to urban planning and transport management, yet is quite challenging because of the lack of readily available data about railway passengers’ citywide flow and event participants’ choice of transportation mode. Previous works have mainly relied on precise but limited data like sensors, AFC (Automated Fare Collection), or smart-card get-on get-off data to estimate the railway passengers, and did not take a holistic multi-dimensional approach to analyze railway traffic congestion caused by the events. To tackle these challenges, we propose a novel interactive Dashboard system that utilizes millions of smartphone GPS records across Japan. The dashboard can estimate and visualize railway passengers for the stations nearby the event venue as well as other relevant event participants’ information. We also introduce a Congestion Index to measure the increase in congestion of stations during events. The dashboard can be highly useful for event organizers, railway administrators, and city planners to assess and compare the impact of big events on railway traffic.

Analysing Urban Tourism Accessibility Using Real-Time Travel Data: A Case Study in Nanjing, China

This study aimed to evaluate the spatial accessibility of tourism attractions in the urban destination city. An analytical framework for assessing urban tourism accessibility at different spatial scales was proposed to provide references on the interaction of urban transport and tourism systems. In addition to the travel time-based measure, a modified gravity model integrating the tourism destination attractiveness, urban transport system characteristics, and tourist demand distribution was developed to evaluate tourism accessibility in this study. Real-time travel data obtained from the Web Maps service were used to take the actual road network operation conditions into consideration and improve the accuracy of estimation results. Taking Nanjing as an example, the analysis results revealed the spatial heterogeneity of tourism accessibility and inequality in tourism resource availability at different levels. Road transport service improvement plays a dominant role in increasing tourism accessibility in areas with insufficient tourism resources, such as the outskirts of the destination city. As for areas with abundant attractions, authorities could pay attention to destination attractiveness construction and demand management in addition to the organization and management of road network operations around attractions during holidays. The results of this study provide a potentially valuable source of information for urban tourism destination management and transport management departments.

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

Characterizing the taxi travel network is of fundamental importance to our understanding of urban mobility, and could provide intellectual support for urban planning, traffic congestion, and even the spread of diseases. However, the research on the interaction network between urban functional area (UFA) units are limited and worthy of notice. Therefore, this study has applied the taxi big data to construct a travel flow network for the exploration of spatial interaction relationships between different UFA units in Shenzhen, China. Our results suggested that taxi travel behavior was more active in UFA units dominated by functions, including residential, commercial, scenic, and greenspace during weekends, while more active in UFA units dominated by industrial function during weekdays. In terms of daily average volume, the characteristics of spatial interaction between the various UFA types during weekdays and weekends were similar. During the morning peak period, the sink areas were mainly distributed in Futian District and Nanshan District, while during the evening peak period, the sink areas were mainly distributed in the southern part of Yantian District, the southwestern part of Longgang District, and the eastern part of Luohu District. The average daily taxi mobility network during weekdays showed a spatial pattern of “dense in the west and north, sparse in the south and east”, exhibiting significant spatial unevenness. Compared with weekdays, the daily taxi mobility network during weekends was more dispersed and the differences in node sizes decreased, indicating that taxi travel destinations were more diverse. The pattern of communities was more consistent with the administrative division during weekdays, indicating that taxi trips are predominantly within the districts. Compared with weekdays, the community pattern of network during weekends was clearly different and more in line with the characteristics of a small world network. The findings can provide a better understanding of urban mobility characteristics in Shenzhen, and provide a reference for urban transportation planning and management.