Detailed understanding of multi-modal mobility patterns within urban areas is crucial for public infrastructure planning, transportation management, and designing public transport (PT) services centred on users’ needs. Yet, even with the rise of ubiquitous computing, sensing urban mobility patterns in a timely fashion remains a challenge. Traditional data sources fail to fully capture door-to-door trajectories and rely on a set of models and assumptions to fill their gaps. This study focuses on a new type of data source that is collected through the mobile ticketing app of HSL, the local PT operator of the Helsinki capital region. HSL’s dataset called TravelSense, records anonymized travelers’ movements within the Helsinki region by means of Bluetooth beacons, mobile phone GPS, and phone OS activity detection. In this study, TravelSense dataset is processed and analyzed to reveal spatio-temporal mobility patterns as part of investigating its potentials in mobility sensing efforts. The representativeness of the dataset is validated with two external data sources - mobile phone trip data (for demand patterns) and travel survey data (for modal share). Finally, practical perspectives that this dataset can yield are presented through a preliminary analysis of PT transfers in multimodal trips within the study area.

In many cities, subways are expanding with new or extended lines being built and put into operations. The prediction of future travel demand in subway with the planned expansion is of significant importance because such information is crucial for new line planning and new network operations. In this study, we identify the determinant features from potential influential factors of passenger travel demand and develop a two-step model for predicting passenger travel demand in expanding subways. The proposed model is tested in an actual subway with a new line being put into operations, and achieves higher prediction accuracy than the benchmark models.

Crowding events, which pose tremendous pressure to city management and society safety, are a typical manifestation of anomalous human mobility in metropolitan areas. However, we are still lacking a comprehensive understanding of the anomalous human mobility in crowding events, which is crucial for preventing crowd disasters and developing sustainable cities and societies. In this study, we analyze the individual and collective human mobility patterns in crowding events using the smart card data of six million subway passengers in Shenzhen city. The discovered individual human mobility patterns reveal the underlying mechanism of crowd formation. The discovered collective human mobility patterns can be employed to anticipate crowding events, offering timely information for transportation and crowd management.

Understanding the patterns of human mobility between cities has various applications from transport engineering to spatial modeling of the spreading of contagious diseases. We adopt a city-centric, data-driven perspective to quantify such patterns and introduce the mobility signature as a tool for understanding how a city (or a region) is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland. First, we use mobility signatures to show that the well-known radiation model is more accurate for mobility flows associated with larger Finnish cities, while the traditional gravity model appears a better fit for less populated areas. Second, we illustrate how the SARS-CoV-2 pandemic disrupted the mobility patterns in Finland in the spring of 2020. These two cases demonstrate the ability of the mobility signatures to quickly capture features of mobility flows that are harder to extract using more traditional methods.

The traffic speed information of an urban road network is generally estimated using the widely available taxi GPS data. However, taxi usages are preponderantly restricted to areas with high population density, which results in limited spatial coverage of collected taxi GPS data. Moreover, the traffic speeds of taxies are not guaranteed to well represent the traffic speeds of other types of vehicles. In this study, we address these issues by introducing an infinite Gaussian mixture model to estimate traffic speed distribution. The variational inference method is employed to deal with the complicated parameter estimation problem. The proposed mixture model simultaneously combines taxi GPS data, bus GPS data, and mobile phone GPS data, which not only generates the mixed traffic-speed distribution of different types of vehicles but also improves the spatial coverage and the quality of traffic speed estimation. Surprisingly, we find that the incorporation of mobile phone GPS data can considerably improve the model’s ability to sense anomalous traffic conditions. Finally, the mixed traffic-speed distribution is validated using the license plate recognition data.

Shuttle buses are generally sent to evacuate stranded passengers during urban metro disruptions. However, when a large number of passengers are transported to the turnaround stations by shuttle buses, it is likely to cause the passengers to wait for the turnaround station. In order to reduce the passenger delay in the whole process of bus bridging and train re-boarding, this paper proposed a coordinated model of bus bridging and passenger flow management and control in response to urban metro failures. The emergency management plan with the minimum passenger delay was obtained by solving the model, and the sensitivity analysis of relevant factors affecting the performance of the model was performed. Taking Shenzhen Metro Line 3 as an example to compare the performance of the traditional model and the coordinated model. The experimental results show that the coordinated model has better performance in reducing passenger delay than the traditional model and the collaborative management scheme can effectively reduce passenger delay; when all passengers can be evacuated, increasing the number of buses has little effect on reducing passenger delay; The time when urban metro starts to operate in a short turning mode and the train departure interval play an important role in reducing passenger delay.

Large crowding events in big cities pose great challenges to local governments since crowd disasters may occur when crowd density exceeds the safety threshold. We develop an optimization model to generate the emergent train stop-skipping schemes during large crowding events, which can postpone the arrival of crowds. A two-layer transportation network, which includes a pedestrian network and the urban metro network, is proposed to better simulate the crowd gathering process. Urban smartcard data is used to obtain actual passenger travel demand. The objective function of the developed model minimizes the passengers’ total waiting time cost and travel time cost under the pedestrian density constraint and the crowd density constraint. The developed model is tested in an actual case of large crowding events occurred in Shenzhen, a major southern city of China. The obtained train stop-skipping schemes can effectively maintain crowd density in its safety range.

In order to reveal the evolutionary mechanism of large passenger flow in urban metro, this paper introduces the concept of anomalous mobility network. Complex network analysis is used to study the structural complexity and dynamic evolution of anomalous mobility networks under ordinary and large passenger flow situations in Shenzhen metro. In addition, this paper proposes an indicator to identify the key nodes of anomalous mobility network. Results show that the anomalous mobility network evolves from long-tailed indegree to long-tailed outdegree with the aggregation and the evacuation of passenger flows. There is a transition process between the scale-free network and the random network. This research can provide a reference for early warning of large passenger flow, passenger flow organization and management in urban metro.

In recent years, with the rapid development of China’s economy and the rapid increase of urban population, the imbalance between supply and demand becomes more notable as various commercial and entertainment activities attract large crowds in urban areas. Although large-scale activities can bring substantial economic benefits to the city and satisfy the spiritual needs of the people, they will also bring tremendous pressure on the transportation facilities around the activity area, leading to negative impacts such as chaotic safety management, traffic congestion.

Traffic flow data collected by traffic sensing devices is crucially important for transportation planning and transportation management. However, traffic sensing devices are typically distributed sparsely in road networks owing to their high installation and maintenance costs. The present study combines license plate recognition (LPR) data with taxi GPS trajectory data to develop a data-driven approach for estimating traffic flow in large road networks. The approach is applied to estimate traffic flow for an actual road network comprising 5,495 road segments using the traffic flow records of only 68 road segments (1.2% of the total). Five-fold cross validation is employed to verify the estimated traffic flow, and the data requirements for implementing the proposed method are analyzed. The developed data-driven approach provides an alternative and cost-efficient way of acquiring additional traffic flow information rather than installing more traffic sensing devices on roads.

Entering big data era, individual GPS trajectory data has created great opportunities for human mobility and collective behavior studies. Individual GPS trajectories can be collected by location-based services on mobile phones. However, GPS data often do not record transportation modes (e.g., walking, riding a bus, or driving a car). In this study, we analyzed the statistical characteristics of individual trajectories and present a collaborative isolation forest (Co-IF) model to identify the transportation modes of mobile phone GPS trajectories. Unlike previous models that identify multiple transportation modes simultaneously, the proposed Co-IF model builds a single-class classifier for each transportation mode and then combines their results. Compared to existing models, the Co-IF model offers competitive performance and shows improved reliability with noisy trajectories.

To investigate different traffic modes for resident’s travel trajectories, a classification model was constructed based on Light Gradient Boosting Machine (LightGBM) to categorize transportation modes according to resident’s GPS trajectories. First, basic trajectory features were extracted, and then more features were obtained using geographic information of public transit network (i.e., Fréchet distance). Subsequently, the features were normalized and screened by the decision tree model. Finally, the screened features were trained and predicted by the model, and a stable prediction result was attained with a five-fold cross-validation method. Results show that geographic information of public transit network could optimize the model’s prediction accuracy. The proposed GPS trajectory recognition method achieved an accuracy of about 90%, which is superior to other machine learning classification models.

A multi-source data-driven traffic control approach is developed to alleviate traffic overload at bottleneck road segments. In the proposed approach, the high-penetration feature of mobile-phone signalling data and the real-time feature of taxi global-positioning-system data are combined to simulate traffic flows in the road network of Shenzhen, a major city of southern China. The road intersections for implementing traffic control schemes are selected by locating the major vehicle sources of the bottleneck road segments, and a genetic algorithm was used to solve the dynamic traffic control schemes. Two important bottleneck road segments in Shenzhen were used as case studies to test the effectiveness of the proposed approach. The authors also propose a method to calibrate the simulated traffic flows when traffic count data are available in the future.

Faced with severe traffic congestions, the level of traffic planning and organization has been paid much attention. As a crucial and fundamental data for traffic planning and traffic organization, travel demand has been an important research topic for a long time. Stepping into the big data era, the fast developments in the research fields of human mobility and complex network offer new methodologies for predicting travel demand and improving transportation networks. In this paper, we review the important works in the areas of human mobility and transportation network, discuss the potential opportunities which bring to transportation engineering, and finally introduce a new interdisciplinary study of the two research areas.

Passenger flow prediction is important for the operation, management, efficiency, and reliability of urban rail transit (subway) system. Here, we employ the large-scale subway smartcard data of Shenzhen, a major city of China, to predict dynamical passenger flows in the subway network. Four classical predictive models: historical average model, multilayer perceptron neural network model, support vector regression model, and gradient boosted regression trees model, were analyzed. Ordinary and anomalous traffic conditions were identified for each subway station by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The prediction accuracy of each predictive model was analyzed under ordinary and anomalous traffic conditions to explore the high-performance condition (ordinary traffic condition or anomalous traffic condition) of different predictive models. In addition, we studied how long in advance that passenger flows can be accurately predicted by each predictive model. Our finding highlights the importance of selecting proper models to improve the accuracy of passenger flow prediction, and that inherent patterns of passenger flows are more prominently influencing the accuracy of prediction.

Subway and bus networks work as an integrated multiplex transportation system and play an indispensable role in modern big cities. Even though a variety of works have investigated the coupling dynamics of multiplex transportation networks, empirical data that validates the determinant coupling factors are still lacking. In this paper, we employ smartcard data of 2.4 million subway and bus passengers in Shenzhen, China to study the coupling dynamics of subway and bus networks. Surprisingly, the coupling of subway and bus networks is not notably influenced by the time-varying speed ratio of the two network layers but is jointly determined by the distribution of travel demands and transportation facilities. Our findings highlight the important role of real travel demand data in analyzing the coupling dynamics of multiplex transportation networks. They also suggest that the speed ratio of different network layers, which was regarded as a key factor in determining coupling strength, has a negligible effect on travelers’ route selections, and thus the coupling dynamics of multiplex transportation networks.

The study of large crowd gatherings combines aspects of longer-range human mobility with site-specific pedestrian dynamics. Recently, substantial progress has been made in understanding the collective behaviors of crowds on the site-specific scale. Yet, the human mobility aspect remains vague in terms of how large crowds come together in the first place. Using high-resolution human mobility data in form of millions, potentially real-time, subway and taxi records, our approach uncovers the mobility patterns involved in large crowd gatherings. In addition, we discriminate anomalous mobility fluxes from ordinary mobility fluxes by introducing the concept of anomalous mobility networks, within which nodes are traffic zones and links are defined via the Jensen-Shannon divergence. Our approach allows for easy identification of occurrence, location and developing stages of crowd formation. Strikingly, within the anomalous mobility networks, we find high-stress crowd density to be preceded by a node in-degree $k_{c}$ surpassing the critical threshold $k_{c}$, typically preceding the maximum crowd density by a couple of hours, enabling us to anticipate large crowd gatherings via a surprisingly simple approach based on the simple network index $k_{in}$.

The urban rail transit network is an important part of an urban public transportation system. First, we generated the network models of the urban rail networks of Beijing and Shenzhen. We used the subway smart card data to estimate the passenger travel demands in the two urban rail transit networks. Next, we analyzed the topological structures of the two urban rail transit networks based on complex network theory and proposed the indices to evaluate the vulnerability of an urban rail transit network. Finally, we generated a two-layer public transportation network to obtain a deeper understanding of the vulnerability of urban rail transit networks. Our empirical results show that the distribution of vulnerable segments is similar in Beijing and Shenzhen rail transit networks. Averagely, Shenzhen urban rail transit network is more vulnerable. The vulnerability of an urban rail network is highly related with its network complexity. Urban bus transit network can reduce the vulnerability of urban rail transit system.

Using mobile phone data to solve the traffic problem is an important application of social computing. In this paper, we used large-scale mobile phone data to estimate dynamical traffic demand and modeled the Internet of Vehicles information network, thus simulating the vehicle’s spatial distribution and the information transport efficiency. First, we studied the largest vehicle cluster and the information transport efficiency of Internet of Vehicles under different vehicle-vehicle commuting distance and internet of vehicles’ using rate. Next, we proposed a bipartite network to analyze the strategy of building information transit towers. Finally, we studied the mechanisms of how vehicle-vehicle commuting distance, Internet of vehicles’ using rate and information transit tower influence internet of vehicles’ coverage and information transport efficiency. We believe that our work can provide useful information for future development of Internet of vehicle.

With the development of urbanization never seen before, the condition of traffic congestion, transit service quality and mergency response capability become more severe. Existing research and engineering practice tend to focus on the analysis and control of phenomena, but lack of probing the underlying causes of the problem.The rise of big data and the proposed source prediction methods provide an important foundation for analysis of the traffic problems’ underlying causes and solving the traffic problems from the source.

Faced with severe traffic congestions, the level of traffic planning and organization has been paid much attention. As a crucial and fundamental data for traffic planning and traffic organization, travel demand has been an important research topic for a long time. Stepping into the big data era, the fast developments in the research fields of human mobility and complex network offer new methodologies for predicting travel demand and improving transportation networks. In this paper, we review the important works in the areas of human mobility and transportation network, discuss the potential opportunities which bring to transportation engineering, and finally introduce a new interdisciplinary study of the two research areas.