This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger p...
With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden pass...
The accurate estimation of real-time passenger flow in urban rail transit (URT) networks plays a crucial role in optimizing the operation and management of urban rail transit systems, with profound implications for daily operation scheduling, passeng...
Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of...
Accurate forecasting of the future transfer passenger flow from historical data is essential for helping travelers to adjust their trips, optimal resource allocation and alleviating traffic congestion. However, current studies have mainly emphasized...
Quantifying and estimating shipping emissions is a critical component of global emission reduction research and has become a growing area of interest in recent years. However, emissions from short-distance passenger ships operating on inter-island ro...
Intercity transport systems have been plagued by low efficiency and overutilization for a long time, due to unhealthy competition among multi-transport modes. Hence, this study aims to estimate the dominant trip distance of intercity passenger transp...
The network operation of the subway can reduce the number of passenger transfers and improve subway operation efficiency. Based on the subway smart card data, this paper proposes an optimal design method for the cross-line operation scheme of subway...
Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in ad...
Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million pa...
Against the backdrop of smart cities and digital cultural tourism, the accurate prediction of urban passenger flow is of great significance for public security management and resource allocation. However, existing studies mostly rely on single data s...
Vehicle ride-sharing commute in smart cities is a service that has changed the way of citizens’ daily life and transportation schedule. Research in vehicle ride sharing aims to provide passengers with a comfortable living and well-being within...
To address the challenges of heterogeneous desensitization requirements, limited system resources, and inefficient resource allocation for structured sensitive data, this paper proposes a data security-oriented multi-objective optimization method for...
The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only...
Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit sys...
China’s transportation industry has made great achievements in the past 40 years of reform and opening up. At the same time, it has gradually accumulated a series of problems. These problems have led to closer and more complex social and econom...
To address the challenges of complex, heterogeneous, and blurred sensitivity boundaries in the sensitive data sources of civil aviation passengers, this paper proposes a hierarchical measurement method. This model integrates information entropy and r...
Understanding the advance ticket purchase behavior of air–rail intermodal passengers is essential for travel demand forecasting, schedule coordination optimization, and revenue management. Using actual booking data, this study investigates pass...
Precise estimation of passenger spatial-temporal trajectory is the basis for urban rail transit (URT) passenger flow assignment and ticket fare clearing. Inspired by the correlation between passenger tap-in/out time and train schedules, we present a...
This study investigates the relationship between passenger volumes and the frequency and size of dwell times for commuter trains. We use data collected by automatic passenger count systems in Stockholm and show that dwell times often take longer than...
This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is e...
Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, add...
Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GP...
The paper focuses on the identification of “bottlenecks” in the passenger handling process at the airports. In the current era of digital transformation and the emergence of Industry 4.0 and 5.0 concepts, optimizing passenger flows throug...
Predicting passenger flows in rail transport systems plays an important role for traffic management centers to make fast decisions during service disruptions. This paper presents an innovative cross-disciplinary approach based on digital twins, deep...
The study results reported here are part of a larger research project that developed a manual for practitioners to improve safety at rail transit platform/train and platform/guideway interfaces. As part of that effort, passenger injury incident data...
This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the a...
With the emergence of all kinds of location services applications, massive location data are collected in real time. A hierarchical fast density clustering algorithm, DBSCAN(density based spatial clustering of applications with noise) algorithm based...
Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to anal...
Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Spa...
The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans a...
With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation ser...
Two-stage least squares (2SLS) regression undergirds much of contemporary geospatial econometrics. Walk-forward validation in time-series forecasting constitutes a special instance of iterative local regression. Two-stage least squares and iterative...
Mexico City’s Metro Line 7 is a critical north–south artery within one of the world’s largest metro systems, yet it suffers from persistent operational inefficiencies, including chronic overcrowding and extended passenger travel tim...
The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an impo...
In this study, a Long Short-Term Memory (LSTM) model with extra variables such as weather conditions and school days was developed within a multi-scale framework in order to forecast passenger flow in both bus and rail systems, covering both regional...
Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely st...
Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraf...
To reduce traffic congestion and pollution, urban rail transit in China has been in a stage of rapid development in recent years. As a result, rail transit service interruption events are becoming more common, seriously affecting the resilience of th...
More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be worki...
In this paper we assess the transit quality of service (QoS) from a user’s standpoint, using smart data. A number of bus lines with different characteristics, operating in the Metropolitan Area of Athens, were chosen as a case study. The data u...
Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and pub...
In companies organizing urban public transport, passengers’ feedback is usually collected personally, by phone, by e-mail, or via the Internet. Business Intelligence tools enable this process by automating the data flow between systems used to...
Accurate estimation of passenger origin–destination (OD) matrices is critical for optimizing public transportation systems, yet conventional methods face challenges, such as incomplete alighting data, high infrastructure costs, and privacy conc...
Rapid urbanization and growing passenger demand are placing increasing pressure on metro systems to provide reliable, sustainable, and passenger-centered urban mobility services. This paper presents Masar, an AI-powered digital twin prototype for Riy...
Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inev...
The sustainable development of urban transport is the key to sustainable urban development. This study analyses and identifies the evaluation indicators affecting Macau’s transport, and analyses the relationship between the relevant evaluation...
Geographical gravity models can be employed to quantitatively describe and predict spatial flows, including migration flows, passenger flows, daily commuting flows, etc. However, how to model spatial flows and reveal the structure of urban traffic ne...
This article examines differences between conventional manual measurements of tram operations and data extracted automatically using the REWIZOR program, based on the Yolo10s algorithm. The study addresses the broader question of how artificial intel...
The bus system is considered a green transportation mode to alleviate traffic congestion in big cities. Understanding travel mobility and network robustness contributes to the design, management and sustainability of the bus system. This paper propos...
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