The temporal geographically-explicit network of public transport in Changchun City, Northeast China

A Comprehensive Vector Dataset of Bus Networks Across China for the Year 2024

Urban public transportation systems facilitate traffic flow and promote sustainable urban development by offering convenient, economical, and environmentally friendly transit services. However, existing research often lacks large-scale bus network data, limiting the potential for comprehensive and in-depth analyses of urban transit systems' complexity and scope. Consequently, large-scale vector data on urban bus networks hold immense value. We designed a set of high-precision algorithms for generating bus network vector data based on electronic map APIs, which include steps such as coordinate transformation, network simplification, station projection, and topology generation. After cross-validation, a vector dataset containing bus stops, routes, and topology for 299 cities in China was generated and stored in Shapefile format (a widely used GIS file). The dataset offers valuable insights for studying network patterns in China's bus systems, supporting international comparative analyses of public transportation systems. Given that China operates the largest bus system globally, this dataset carries substantial academic and practical significance, providing data-driven insights for optimizing network structures, enhancing transportation efficiency, and informing policy-making.

ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions

This paper presents the Zurich Transit Bus (ZTBus) dataset, which consists of data recorded during driving missions of electric city buses in Zurich, Switzerland. The data was collected over several years on two trolley buses as part of multiple research projects. It involves more than a thousand missions across all seasons, each mission usually covering a full day of operation. The ZTBus dataset contains detailed information on the vehicle's power demand, propulsion system, odometry, global position, ambient temperature, door openings, number of passengers, dispatch patterns within the public transportation network, etc. All signals are synchronized in time and include an absolute timestamp in tabular form. The dataset can be used as a foundation for a variety of studies and analyses. For example, the data can serve as a basis for simulations to estimate the performance of different public transit vehicle types, or to evaluate and optimize control strategies of hybrid electric vehicles. Furthermore, numerous influencing factors on vehicle operation, such as traffic, passenger volume, etc., can be analyzed in detail.

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference

A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human behavior and high data requirements. In this work, we propose a deep-learning framework PL-TARMI, which leverages easily accessible data to achieve accurate fine-grained traffic crash risk map inference. Specifically, we integrate the satellite image and road network image, combine with other accessible data (e.g., point of interest distribution, human mobility data, traffic data, etc.) as input, and finally obtain the pixel-level traffic crash risk map, which could provide more reasonable traffic crash prevention guidance with a lower cost. Extensive experiments on real-world datasets demonstrate the effectiveness of PL-TARMI.

A BIM Based Hybrid 3D Indoor Map Model for Indoor Positioning and Navigation

Accurate and fast indoor Location-Based Services (LBS) is very important for daily life and emergency response. Indoor map is the basis of indoor LBS. The model construction and data organization of indoor map are the key scientific problems that urgently need to be solved in the current indoor LBS application. In recent years, hybrid models have been used widely in the research of indoor map, because they can balance the limitations of single models. However, the current studies about hybrid model pay more attention to the model accuracy and modeling algorithm, while ignoring its relationship between positioning and navigation and its practicality in mobile indoor LBS applications. This paper addresses a new indoor map model, named Building Information Modeling based Positioning and Navigation (BIMPN), which is based on the entity model and the network model. The highlight of BIMPN is that it proposes a concept of Step Node (SN) to assist indoor positioning and navigation function. We developed the Mobile Indoor Positioning and Navigation System (MIPNS) to verify the practicability of BIMPN. Results indicate that the BIMPN can effectively organize the characteristics of indoor spaces and the building features, and assist indoor positioning and navigation. The BIMPN proposed in this paper can be used for the construction of indoor maps and it is suitable for mobile indoor positioning and navigation systems.