Zhenliang Ma

• PhD
• Associate Professor at KTH Royal Institute of Technology

Revenue reconciliation is an important problem in allocating the fare revenues to different lines and operators in urban rail transit systems. This paper proposes a data-driven fusion method for fare reconciliation in public transport using mobile signal, smart card, and train operation data. It makes the best use of the complementary advantages of...

Bus delays significantly affect urban public transportation by reducing operational efficiency and incurring high costs. Understanding the causes of these delays is essential for developing targeted mitigation strategies. While traditional research focuses on correlation-based analysis, it often fails to uncover the underlying causal mechanisms. Th...

Incentive-based strategies tailored to individual preferences can motivate commuters to adopt public transit, potentially easing road congestion and fostering ecofriendly urban travel. However, understanding diverse responses to these incentives has been challenging due to low survey participation and certain homogeneity assumptions, limiting our k...

Understanding travelers' route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs)...

The pressing need to reduce greenhouse gas emissions triggers the imperative for efficient travel demand management. Previous studies have explored budget-based and aggregated incentive programs, which place a significant financial burden on governments and tend to be limited in contributing to effective behavior change in practice due to budget is...

This paper studies the estimation of dynamic route choice behavior of drivers with incomplete fixed location-based sensor data, such as radio frequency identification (RFID) data. Unlike global positioning system (GPS) data providing continuous vehicle trajectories, the location-based RFID sensors record vehicles only when they pass by but may not...

Urban rail transit systems in many cities are experiencing crowding during peak periods due to rapid population growth. Incentive-based demand management strategies aim to better utilize the available capacity by shifting peak travel to off-peak periods. Various deployments have demonstrated the crowding-reduction potential of incentives in reducin...

The rapid expansion of metro networks in metropolitan areas has introduced complexities in passenger path choice behavior across diverse operation conditions. While existing research addresses this challenge by framing it as a route choice or trajectory reconstruction problem employing various data sources, the effectiveness of single-model, data-d...

The rapid expansion of metro networks in metropolitan areas has introduced complexities in revenue reconciliation, making it challenging to fairly allocate fare revenues among various services and operators. Existing research often frames this issue as a route choice or trajectory reconstruction problem, utilizing diverse data sources. However, sin...

Accurately and timely predicting pedestrian crossing intentions in real-time is critical for operating intelligent vehicles on roads. Although existing models achieve promising accuracy using complex models and video image data, they are constrained for real-time practical use given the high model complexity, time-consuming data preprocessing, and...

To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotem...

Conventional uni-modal transportation recommendation systems focused on single modes of transportation are limited in providing satisfactory solutions since passengers often undertake journeys involving multiple modes. Multi-modal transportation recommendation systems are becoming increasingly popular within navigation applications. However, these...

Prompt and accurate identification of anomalies in passenger flow within metro systems is crucial for safety, security, and operational efficiency. However, traditional anomaly detection methods often struggle to achieve high accuracy and low latency when constrained by limited labeled data for online applications. This study presents a straightfor...

Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to ex...

Behaviorally oriented activity-travel choices (ATC) modeling is a principal part of travel demand analysis. Traditional econometric and rule-based methods require explicit model structures and complex domain knowledge. While several recent studies used machine learning models, especially adversarial inverse reinforcement learning (IRL) models, to l...

Understanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge g...

Online demand prediction plays an important role in transport network services from operations, controls to management, and information provision. However, the online prediction models are impacted by streaming data quality issues with noise measurements and missing data. To address these, we develop a robust prediction method for online network-le...

Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate e...

The paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes with a terminal station at a common hub, allowing a portion of (or all) the fleet to be shared among the routes belonging to the hub (shared fleet) as needed. The shared fleet is dispatched on an on-demand ba...

Urban railway systems in many cities are facing increasing levels of crowding and operating near capacity. Crowding at stations and on trains is a concern due to its impact on safety, service quality, and operating efficiency. Denied boarding is becoming a key measure of the impact of near-capacity operations on customers, and it is fundamental for...

Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused...

Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research...

This paper presents a joint optimization of the timetable, bus formation, and vehicle scheduling in a flexible public transport (PT) system that utilizes autonomous modular vehicles (AMVs). In this system, AMVs have the capability to detach or join with each other at intermediate stops along the route to dynamically adjust the bus formation (capaci...

Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in managing surging demand and informing effective operations planning and controls in the network. Existing studies have primarily focused on identifying the source of anomalies at a single station by analysing the time-series characteristics of passenger flow. How...

Providing real-time crowding information in urban railways would enable informed travel decisions and encourage cooperative behavior of passengers, as well as improve operating efficiency and safety. However, the problem of real-time crowding prediction is not trivial because of the unavailability of ground-truth crowding data, particularly for the...

Understanding human mobility in urban areas is important for transportation, from planning to operations and online control. This paper proposes the concept of user-station attention, which describes the user’s (or user group’s) interest in or dependency on specific stations. The concept contributes to a better understanding of human mobility (e.g....

The increasing availability of travel trajectory data allows for a better understanding of travel behavior. In the individual mobility analysis, the problem of next trip prediction assumes a central role and is beneficial for applications such as personalized services and mobility management. This paper addresses the next trip prediction problem wi...

The current sharing economy suffers from system-wide deficiencies even as it produces distinctive benefits and advantages for some participants. The first generation of sharing markets has left us to question: Will there be any workers in the sharing economy? Can we know enough about these technologies to regulate them? Is there any way to avoid th...

Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies analyze the train delay factors using a single, generic regression equation, restricting their capability in accounting for heterogeneous impacts of spatiotemporal factors on arrival...

The paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes with a terminal station at a common hub, allowing a portion of (or all) the fleet to be shared among the routes belonging to the hub (shared fleet) as needed. The shared fleet is dispatched on an on-demand ba...

Understanding passengers' path choice behavior in urban rail systems is a prerequisite for effective operations and planning. This paper attempts bridging the gap by proposing a probabilistic approach to infer passengers' path choice behavior in urban rail systems using a large-scale smart card data. The model uses latent classes and panel effects...

Understanding human mobility in urban areas is important for transportation, from planning to operations and online control. The availability of large-scale and multi-source mobility data (e.g., smart card data, GPS data) facilitates the understanding of human mobility at a deep level but also brings challenges in organizing and analyzing these dat...

The occurrence of incidents seriously affects the operation of the whole urban railway system and passengers' travel experience. Accurate delay prediction is important for traffic control and management under incidents. Few studies were reported on incident prediction in urban railway systems due to the unexpected nature of incidents and the lack o...

Mining passenger demand pattern under disruptions is important for deploying targeted measures and reducing disruption impacts in urban rails. The study develops a robust principal component analysis model with an accelerated proximal gradient algorithm to estimate irregular passenger demand patterns under disruptions. It takes inputs of the networ...

Revenue reconciliation is an important problem in allocating the fare revenues to different lines and operators in urban rail transit systems. The paper proposes a data-driven fusion method for fare reconciliation in public transport using mobile signal, smart card, and train operation data. It makes the best use of the complementary advantages of...

Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers' behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers' responses to incentives, they focused on pa...

Eye contact is essential in transmitting information and intention in the wild environment (e.g., urban streets or parking lots) with mixed vehicles and pedestrians. Compared with the vision image data, the human skeleton data are deemed to be robust to unconstrained surroundings and illumination. However, the skeleton graph-based approaches are ma...

Shared mobility on demand (MoD) services are receiving increased attention as many high volume ride-hailing companies are offering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises further operational opportunities to benefit from these developments as AVs enable a centrally oper...

Reinforcement learning (RL)‐based models have been widely studied for traffic signal control with objectives, such as minimizing vehicle delay and queue length, maximizing vehicle throughput, and improving road safety, through tailored reward designs. Despite the advancements in RL‐based signal control models for car traffic, limited research focus...

This paper proposes an ex post path choice estimation framework for urban rail systems using an aggregated time-space hypernetwork approach. We aim to infer the actual passenger flow distribution in an urban rail system for any historical day using the observed automated fare collection (AFC) data. By incorporating a schedule-based dynamic transit...

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for short-distance journeys using cars or...

Real-time prediction of train arrivals is important for proactive traffic control and information provision in passenger rails. Despite many studies in predicting arrival times or delays at stations, they are essentially the next-step time series prediction problem which may limit their applications in practice. For example, passengers on the train...

Unplanned disruptions bring challenges to urban railway system operations because of their impacts on safety, operation efficiency, and service quality. Identifying the contributing factors of operation delays and affected areas under unplanned disruptions is critical for agencies to make effective and informed management decisions. Despite its imp...

The paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes that have a terminal station at a common hub, that allows a portion of (or all) the fleet to be shared among the routes belonging to the hub (shared fleet) as needed. The shared fleet is dispatched on an on-d...

Short-term passenger flow prediction under planned events is important to reduce passenger delay and ensure operational safety in metro systems. However, most studies make predictions under normal conditions. The study proposes a naïve Bayes transition model for short-term passenger flow prediction under planned events. The target prediction scenar...

Short-term origin–destination (OD) flow prediction is vital for operations planning, control, and management in urban railway systems. While the entry and exit passenger demand prediction problem has been studied in various studies, the OD passenger flow prediction problem receives much less attention. One key challenge for short-term OD flow predi...

The ‘sharing’ business models and on-demand services have been altering city dwellers’ travel habits from buying the means of transport to buying mobility services based on needs. The capability to proactively provide personalized services (e.g., travel recommendations and dynamic pricing) is the future of smart multimodal mobility systems, in whic...

Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Traveled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popul...

unplanned disruptions are not avoidable. From a passenger perspective, the duration of delays and other impacts is a major concern as passenger delays are not necessarily the same as train delays. Although an incident is cleared, the disruption impact on passengers can continue, as it may take a longer time for the operations to return to normal. F...

Unplanned disruptions bring challenges to the urban railway system operations due to their impacts on safety, operation efficiency, and service quality. Identifying the contributing factors of operation delays and affected areas under unplanned disruptions is critical for agencies to make effective and informed management decisions. Despite its imp...

Ride-haling services are becoming popular in cities, especially for young people. Although many studies have investigated ride-hailing usage behavior for the general population, few studies focus on the university population (key users). This paper conducts an online survey to investigate students’ usage behavior of ridehailing services in Chinese...

The free-floating bicycle sharing system (FFBSS) service is important in the urban mobility ecosystem. Although many studies conducted surveys to understand the usage characteristics of FFBSS service for the general population, few studies focus on the usage behavior of university students. This paper examines the adoption and usage behavior of FFB...

The free-floating bicycle sharing system (FFBSS) service has been becoming an important and popular mode for travel in the urban mobility ecosystem. Although many studies conducted surveys to understand the usage characteristics of FFBSS service for the general population, few studies focus on the usage behavior of university students. A total of 2...

The imbalance between supply and demand causes low operational efficiency for Transportation Network Company (TNC) services. Demand management is an efficient way to re-distribute requests in space and time dimensions, and eventually, enhance service operating efficiency. The ability to understand and influence travel behavior is one of the most si...

Data quality is the foundation of data-driven applications in transportation. Data problems such as missing and invalid data could sharply reduce the performance of the methods used in these applications. Although there exist plenty of studies related to data quality issues, they only focus on missing or invalid data caused by infrastructure failur...

Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events...

Data quality is essential for its authentic usage in analysis and applications. The large volume of automated collection data inevidently suffers from data quality issues including data missing and invalidity. This paper deals with an invalid data problem in the automated fare collection (AFC) database caused by the erroneous association between th...

Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studie...

Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and t...

Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studie...

The free-floating bicycle sharing system (FFBSS) service is important in the urban mobility ecosystem. Although many studies conducted surveys to understand the usage characteristics of FFBSS service for the general population, few studies focus on the usage behavior of university students. This paper examines the adoption and usage behavior of FFB...

Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and t...

Increasing ridership in the urban rail systems in major cities is outpacing their designed capacity. Promotion based demand management can facilitate better utilization of the available capacity of the existing system when the investment and opportunity to expand the system are limited. While several studies address short-term behavioral responses...

Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Travelled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popu...

Transportation demand management, long used to reduce car traffic, is receiving attention among public transport operators as a means to reduce congestion in crowded public transportation systems. Though far less studied, a more structured approach to public transport demand management (PTDM) can help agencies make informed decisions on the combina...

Short-term metro passenger flow prediction is vital for the operation and management of metro systems. Most studies focus on the higher prediction accuracy with statistical and machine learning methods, but little attention has been paid to the prioritization and selection of feature variables, especially for different metro station types. This stu...

42 43 Word count: 6029 words + 6 tables (1500) = 7,529 words 44 45 46 47 ABSTRACT 49 Short-term metro passenger flow prediction is vital for the metro operation and management. 50 Most studies focus on the higher prediction accuracy with statistical and machine learning 51 methods, but little attention has been paid to the prioritization and select...

Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types...

This paper proposes a general network performance model (NPM) for monitoring the performance of urban rail systems using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination demand, operations, route choice, and ef...

Passenger path choice in highly congested metro systems is affected by crowding in vehicles and denied boarding to trains. This paper deals with a special situation where passengers choose to stay in the train longer than what would be preferable under normal conditions and transfer at a station further along the line in order to travel backwards a...

Short-term metro passenger flow prediction is vital for the metro operation and management. Most studies focus on the higher prediction accuracy with statistical and machine learning methods, but little attention has been paid to the prioritization and selection of feature variables, especially for different metro station types. The study aims to a...

This paper proposed a general data-driven Network Performance Model (NPM) for daily network performance monitoring using smart card data. The major component of NPM is a schedule-based network loading model with strict capacity constraints. The potential applications of NPM include estimating crowding patterns, diagnosing crowding sources and evalu...

Transportation Network Company (TNC) services have grown exponentially in recent years in terms of both, ridership, and business models. The TNC growth has outpaced the capacity of cities to oversee their operations, with many unprepared to deal with the consequences of this growth in terms of increase in congestion, and impact on other transportat...

Growing urbanization causes increase in crowding in many urban railway systems. Providing real-time crowding information would enable informed travel decisions and encourage cooperative behavior of passengers, as well as improve operating efficiency and safety. However, the problem of real-time crowding prediction is not trivial due to the unavaila...

Dynamic interlining of buses is an operational strategy for routes that have a terminal station at a common hub. The strategy keeps a portion of the fleet unassigned to any specific route and allows them to be shared among the routes belonging to the hub (shared fleet). The shared fleet is then dispatched on an on-demand fashion to avoid delays and...

This paper proposed a simulation-based optimization framework to identify the route choices patterns with an event-driven transit network loading model. Five optimizers of three main brunches of SBO methods are applied in this paper for comparative analysis, which includes Nelder-Mead Simplex Algorithm (NMSA), Mesh Adaptive Direct Search (MADS), Si...

Urban rail services are the principal means of public transportation in many cities. To understand the crowding patterns and develop efficient operation strategies in the system, obtaining path choices is important. This paper proposed an assignment-based path choice estimation framework using automated fare collection (AFC) data. The framework cap...

Studying mobility patterns in public transport systems is critical for multiple applications from strategic planning to operations control to information provision. Unveiling and understanding the underlying (unobserved) mechanism governing the generation of (observed) mobility patterns are challenging. Considering the recurrent human travel activi...