Occitania Transport Mode (OCC-TM) is a mobility dataset collected using a smartphone application developed as part of Vilagil research project. This application passively collects GPS positions and accelerometer signals from the smartphone. This study focuses on data collected by a 25-year-old male user. This user then added a label corresponding to the mode of transportation (walk, still, car, bus, bike, train or metro) of each observation point. The smartphone used for collection is a Samsung Galaxy A32 with the Android 11 operating system.

The dataset data was collected by a single user in a discontinuous manner from July 26, 2022 to August 10, 2022. This user moved around the Occitania region in the south of France (between Toulouse and Montpellier), noting for each trip the start time, end time, and mode of transportation used.

The .zip file contains two folders:

• raw_data: raw accelerometer, location and label data in .csv format

• processed_data: feature dataset in .csv format

Please cite the paper below in your publications if it helps your research:

TODO

Context

Identify user’s transportation modes through observations of the user, or observation of the environment, is a growing topic of research, with many applications in the field of Internet of Things (IoT). Transportation mode detection can provide context information useful to offer appropriate services based on user’s needs and possibilities of interaction.

Content

Initial data pre-processing phase: data cleaning operations are performed, such as delete measure from the sensors to exclude, make the values of the sound and speed sensors positive etc...

Furthermore some sensors, like ambiental (sound, light and pressure) and proximity, returns a single data value as the result of sense, this can be directly used in dataset. Instead, all the other return more than one values that are related to the coordinate system used, so their values are strongly related to orientation. For almost all we can use an orientation-independent metric, magnitude.

Acknowledgements

A sensor measures different physical quantities and provides corresponding raw sensor readings which are a source of information about the user and their environment. Due to advances in sensor technology, sensors are getting more powerful, cheaper and smaller in size. Almost all mobile phones currently include sensors that allow the capture of important context information. For this reason, one of the key sensors employed by context-aware applications is the mobile phone, that has become a central part of users lives.

Inspiration

User transportation mode recognition can be considered as a HAR task (Human Activity Recognition). Its goal is to identify which kind of transportation - walking, driving etc..- a person is using. Transportation mode recognition can provide context information to enhance applications and provide a better user experience, it can be crucial for many different applications, such as device profiling, monitoring road and traffic condition, Healthcare, Traveling support etc..

Original dataset from: Carpineti C., Lomonaco V., Bedogni L., Di Felice M., Bononi L., "Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity", in Proceedings of the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 2018 [Pre-print available]

“Self-powered” refers to walking, running, and biking.

An overview of related research on Wi-Fi based transportation detection.

The effectiveness of large language models (LLMs) in transportation mode detection remains underexplored, creating a significant research gap in understanding how these models process trajectory data. This study uses the Geolife dataset to investigate the ability of pre-trained and fine-tuned LLMs to detect transportation modes across 14 trajectory formats, categorized into overview information, coordinate-based, and spatial encoding. Meanwhile , two response strategies are compared: direct answer and Chain-of-Thought (CoT) reasoning. The results show that fine-tuning significantly enhances the classification performance for all trajectory formats. Among the evaluated formats, the coordinate-based format with timestamps achieves the highest accuracy of 85.2% after fine-tuning using the direct answer strategy. The direct answer strategy proves to be more effective than the CoT strategy, reaching an average 49.0% improvement in accuracy via fine-tuning. Additionally, the model exhibits systematic misclassification patterns, reflecting challenges in distinguishing between transportation modes with similar movement characteristics. Furthermore, our analysis reveals that hallucinations are prevalent in CoT responses, particularly of the types of input-conflicting hallucinations and factual inaccuracies, which increase the likelihood of misclassification. These findings highlight the potential of LLMs in transportation mode detection while emphasizing the need for enhanced trajectory formats, improved response strategies, and strategies to mitigate hallucinations.

SHL Dataset and HTC Dataset for KDTMD model

This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.In total 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals.The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 hours of labelled data and 17562 km of traveled distance. The potential applications arising from this dataset include:Machine-learning systems to automatically recognize modes of transportations from mobile phone dataRoad condition analysis and recognitionTraffic conditions analysis and recognition.Assessment of Google’s activity and transportation recognition API in comparison to custom algorithmsProbabilistic mobility modellingActivity recognition (e.g. automatic detection of eating and drinking)Novel localization techniques using dynamic fusion of sensorsRadio signal propagation analsisImage-based activity and transportation mode recognition The current recommended publication regarding the dataset is [1]. The current recommended publication regarding the application which was used to collect the dataset is [2].[1] H. Gjoreski, M. Ciliberto, L. Wang, F. J. Ordoñez Morales, S.Mekki, S.Valentin, D. Roggen, “The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices”, In IEEE Access, 2018[2] M. Ciliberto, F. J. Ordoñez Morales, H. Gjoreski, D. Roggen, S.Mekki, S.Valentin. “High reliability Android application for multidevice multimodal mobile data acquisition and annotation.” In ACM Conference on Embedded Networked Sensor Systems. ACM, 2017.We recommend to refer to the dataset as follows in your publications:Use at least once the complete name: “The University of Sussex-Huawei Locomotion and Transportation Dataset” or “The Sussex-Huawei Locomotion and Transportation Dataset“. You may introduce the acronym of the dataset as well: “The University of Sussex-Huawei Locomotion and Transportation (SHL) Dataset“.Subsequently, you may refer to the dataset with its acronym: “The SHL Dataset“.

Performance measures for random forest model under various feature sets.

Context

This GPS trajectory dataset was collected in (Microsoft Research) Geolife project by 178 users in a period of over four years (from April 2007 to October 2011). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of 1,251,654 kilometers and a total duration of 48,203 hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point.

Content

This dataset recoded a broad range of users’ outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling.

Data Format - Trajectory file Every single folder of this dataset stores a user’s GPS log files, which were converted to PLT format. Each PLT file contains a single trajectory and is named by its starting time. To avoid potential confusion of time zone, we use GMT in the date/time property of each point, which is different from our previous release. - PLT format: Line 1…6 are useless in this dataset, and can be ignored. Points are described in following lines, one for each line. Field 1: Latitude in decimal degrees. Field 2: Longitude in decimal degrees. Field 3: All set to 0 for this dataset. Field 4: Altitude in feet (-777 if not valid). Field 5: Date - number of days (with fractional part) that have passed since 12/30/1899. Field 6: Date as a string. Field 7: Time as a string. Note that field 5 and field 6&7 represent the same date/time in this dataset. You may use either of them. Example: 39.906631,116.385564,0,492,40097.5864583333,2009-10-11,14:04:30 39.906554,116.385625,0,492,40097.5865162037,2009-10-11,14:04:35 - Transportation mode labels Possible transportation modes are: walk, bike, bus, car, subway, train, airplane, boat, run and motorcycle. Again, we have converted the date/time of all labels to GMT, even though most of them were created in China. Example: Start Time End TimeTransportation Mode 2008/04/02 11:24:21 2008/04/02 11:50:45 bus 2008/04/03 01:07:03 2008/04/03 11:31:55 train 2008/04/03 11:32:24 2008/04/03 11:46:14 walk 2008/04/03 11:47:14 2008/04/03 11:55:07 car

First, you can regard the label of both taxi and car as driving although we set them with different labels for future usage. Second, a user could label the transportation mode of a light rail as train while others may use subway as the label. Actually, no trajectory can be recorded in an underground subway system since a GPS logger cannot receive any signal there. In Beijing, the light rails and subway systems are seamlessly connected, e.g., line 13 (a light rail) is connected with line 10 and line 2, which are subway systems. Sometimes, a line (like line 5) is comprised of partial subways and partial light rails. So, users may have a variety of understanding in their transportation modes. You can differentiate the real train trajectories (connecting two cities) from the light rail trajectory (generating in a city) according to their distances. Or, just treat them the same.

More: User Guide: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/User20Guide-1.2.pdf

Citation

Please cite the following papers when using this GPS dataset. [1] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791-800.

[2] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321. [3] Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32-40.

Inspiration

This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation.

The dataset contains locations and attributes of Vehicle Detection Systems, created from a database provided by the District Department of Transportation.

Each study reviewed is here catalogued as follows.· Level of difficulty: Classification Task, Number and List of Classes.· Approach: Method and Main Features.· Performance: Score, Metric, Validation Method.· Realism of dataset: Ground Truth, Person-day, Respondents, Observations, Collection Time, Area, Smartphone App.· Sensors involved: AGPS, Inertial Navigation Systems (INS), Geographic Information Systems (GIS), Data Fusion.

Rescue Transportation AI System

## Overview

Rescue Transportation AI System is a dataset for object detection tasks - it contains Person 2M2f annotations for 9,852 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Intelligent transportation systems (ITS) are composed of functionalities that aim to reduce traffic jams and detect anomalies. These functionalities include vehicle detection and classification that represent critical tasks and require a high degree of accuracy to ensure the extracted information's reliability. Therefore, we present a key data set related to the detection of vehicles in Morocco, nicknamed MoVITS. The data provided in this paper is collected using a stereo-based vision system, and it is useful for vehicle detection and classification by using different methods for many purposes. This data set is intended to support researchers, stakeholders, and the general public interested in ITS. The vehicle video dataset incorporated with deep learning, transfer learning, and fine-tuning can provide outstanding vehicle detection and classification results.

With the progress in sensor technology and the spread of mobile devices, transportation mode detection (TMD) is gaining importance for health and urban traffic improvements. As mobile devices become more lightweight, they require more efficient, low-power models to handle limited resources effectively. Despite extensive research on TMD, challenges remain in capturing non-stationary temporal dynamics and nonlinear fitting capabilities. Additionally, many existing models exhibit high space complexity, making lightweight deployment on devices with limited computing and memory resources difficult. To address these issues, we propose a novel deep TMD model based on discrete wavelet transform (DWT) and knowledge distillation (KD), called KDTMD. This model consists of two main modules, i.e., DWT and KD. For the DWT module, since non-stationary time variations and event distribution shifts complicate sensor time series analysis, we use the DWT modules to disentangle the sensor time series into two parts: a low-frequency part that indicates the trend and a high-frequency part that captures events. The separated trend data is less influenced by event distribution shifts, effectively mitigating the impact of non-stationary time variations. For the KD module, it includes the teacher model and student model. Specifically, for teacher model, to address the nonlinearities and interpretability, we incorporate T-KAN, which is composed of multiple layers of linear KAN that employ learnable B-spline functions to achieve a richer feature representation with fewer parameters. For student model, we develop the S-CNN, which is trained efficiently by T-KAN through KD. The KDTMD model achieves 97.27% accuracy and 97.29% F1-Score on the SHL dataset, and 96.56% accuracy and 96.72% F1-Score on the HTC dataset. Additionally, the parameters of the KDTMD model are only about 10% of the smallest baseline.

The global Transport Security System market is projected to experience robust growth, reaching an estimated market size of approximately USD 45,000 million in 2025. This expansion is driven by a Compound Annual Growth Rate (CAGR) of 12.5% over the forecast period of 2025-2033, indicating a dynamic and expanding sector. The increasing need to safeguard passengers, cargo, and critical infrastructure across various transportation modes—including airways, waterways, roadways, and railways—is a primary catalyst. Escalating security threats, coupled with the proliferation of advanced technologies such as AI-powered surveillance, IoT integration for real-time monitoring, and advanced threat detection systems, are fueling market adoption. The emphasis on enhancing traveler experience through secure and efficient transit operations further propels investment in these sophisticated systems. The market is segmented into Vehicle and On-board Equipment Security, and Workers and Things Security, with both segments demonstrating significant potential. Geographically, North America and Europe currently lead in market share due to established infrastructure and stringent security regulations. However, the Asia Pacific region, particularly China and India, is anticipated to exhibit the highest growth rate, driven by rapid urbanization, increasing transportation networks, and a growing awareness of the importance of transport security. Key players like Siemens Mobility, Thales Group, and Honeywell Security are at the forefront, innovating and expanding their portfolios to meet the evolving demands for comprehensive and integrated transport security solutions. While the market is poised for significant expansion, potential restraints such as high initial investment costs and the need for skilled personnel for implementation and maintenance require careful consideration.

The global Vehicle Occupancy Detection System (VODS) market is experiencing robust growth, driven by increasing urbanization, escalating traffic congestion, and a rising demand for intelligent transportation systems (ITS). Governments worldwide are investing heavily in infrastructure improvements to optimize traffic flow and enhance road safety, fueling the adoption of VODS. The market is segmented by application (passenger cars and commercial vehicles) and type of installation (fixed and mobile). Fixed installations, primarily used in toll plazas and intelligent transportation systems, currently dominate the market share. However, mobile installations are witnessing rapid growth due to advancements in sensor technology and the increasing integration of VODS into advanced driver-assistance systems (ADAS) and autonomous vehicles. Key players in this competitive landscape include Siemens, Indra Sistemas, NEC Corporation of America, TransCore, and others, constantly innovating to offer improved accuracy, reliability, and cost-effectiveness. The market is geographically diverse, with North America and Europe leading in adoption, followed by Asia-Pacific, driven by significant infrastructural development projects in countries like China and India. The market's growth trajectory is further bolstered by the increasing demand for real-time traffic information and data-driven decision-making in traffic management. Growth within the VODS market is projected to continue at a healthy pace over the forecast period (2025-2033). This sustained expansion can be attributed to several factors, including the ongoing development of smart cities initiatives worldwide, the rising adoption of connected and autonomous vehicles, and the increasing focus on improving public transportation efficiency. However, challenges remain, including the high initial investment costs associated with VODS implementation and the need for robust cybersecurity measures to protect sensitive data collected by these systems. Nevertheless, the long-term benefits of improved traffic management, reduced congestion, and enhanced safety are expected to outweigh these challenges, driving consistent market growth. The market is expected to see a gradual shift towards more sophisticated and integrated solutions, incorporating advanced analytics and machine learning to optimize traffic flow and improve overall transportation efficiency.

This dataset presents aerial video and vehicle trajectory data collected during a phantom traffic jam experiment with the Swiss television (SRF) at the driving test center TCS Derendingen, Solothurn, from March 12th 2024. 14 vehicles were recorded for a total duration of 40 minutes with a drone from above, and vehicle trajectories were extracted using computer vision and Kalman filtering methodology.
The observed vehicles differ by their power train (combustion, electric, hybrid), gearbox (manual, automatic), and equipment with advanced driver assistance systems.

The folder VIDEOS contains fifteen video recordings covering a duration of around one hour (01:02:56) and 94,423 frames in total, at a framerate of 25 frames per second, and with a resolution of 3840 x 2160 pixels. The videos show more than 14 vehicles driving on the ring road of the driving test center TCS Derendingen (Solothurn, Switzerland) during various experiments. The videos are provided in MOV format.

The folder ANNOTATIONS provides object annotations for each video and frame a list of rectangular annotations that envelop a vehicle, generated by 18 different object detection models.
The annotations are provided as zipped CSV files, separated by the tabulator symbol.
Each row consists of eight columns:

• (1) frame number
• (2) annotation type (following the DOTA dataset classification system)
• (3) rectangle center x coordinate (in pixels)
• (4) rectangle center y coordinate (in pixels)
• (5) rectangle width (in pixels)
• (6) rectangle height (in pixels)
• (7) rectangle angle (in radians)
• (8) annotation confidence (between 0.0 and 1.0, provided by the neural network).

The folder TRAJECTORIES contains for each video, vehicle, and frame an exact vehicle position, speed, acceleration, and headway. The vehicle trajectories are provided as zipped CSV files, separated by the comma symbol.
Each row consists of 18 columns:

• (1) Vehicle_ID
• (2) Frame_ID
• (3) Global_Time (in seconds)
• (4) Cartesian_X (in meter)
• (5) Cartesian_Y (in meter)
• (6) Polar_X (in radians)
• (7) Polar_Y (in meter)
• (8) Lane_X (in meter)
• (9) Lane_Y (in meter)
• (10) v_Length (in meter)
• (11) v_Width (in meter)
• (12) v_Vel (in m/s)
• (13) v_Angle (in radians)
• (14) v_AngleVel (in rad/s)
• (15) Proceeding (Vehicle_ID of proceeding vehicle)
• (16) Space_Hdwy (in meter)
• (17) Time_Hdwy (in seconds)
• (18) v_Accel (in m/s^2)

The code to extract and process the vehicle trajectories from the videos is available on GitHub: https://github.com/DerKevinRiehl/trajectory_analysis

The TV-show episode of "Einstein" in Swiss television "SRF" from May 2nd 2024 can be found here: https://www.srf.ch/play/tv/einstein/video/stau-was-hilft-gegen-den-verkehrskollaps?urn=urn:srf:video:63965781-c7ea-4033-9827-be4275f1cba5

Acknowledgements

We thank the Schweizer Radio und Fernsehen (SRF, Swiss Radio and Television, tv-show "Einstein"), Adrian Winkler, Laurin Merz, and Andrea Fischli for their support when organizing participants and vehicles for the experiment, and filming and documenting it for the Swiss public. We thank Andre Greif and the TCS Driver Training Center in Derendingen, Solothurn (Switzerland) for hosting our experiment.

Publications

• "Aerial Video & Trajectory Dataset Of Vehicles On Circular Road" (In Submission in Data In Briefs)
• "Consistent Vehicle Trajectory Extraction From Aerial Recordings Using Oriented Object Detection" (In Submission in Scientific Reports)

Smart Transport System Market Outlook

The global market size for Smart Transport Systems (STS) was valued at around USD 42.8 billion in 2023 and is projected to reach approximately USD 108.3 billion by 2032, growing at a CAGR of 10.8% during the forecast period. This significant growth is driven by the increasing need for efficient traffic management solutions, the burgeoning urban population, and the rising investments in smart city projects worldwide.

One of the primary growth factors for the Smart Transport System market is the escalating urbanization and the associated traffic congestion problems. As cities expand, the existing transportation infrastructure struggles to cope with the increasing number of vehicles, leading to traffic jams, longer commute times, and higher pollution levels. Smart Transport Systems, which integrate advanced technologies like IoT, AI, and data analytics, offer innovative solutions for real-time traffic management, reducing congestion, and improving overall transportation efficiency. Governments and city planners are increasingly adopting these systems to enhance urban mobility and ensure sustainable development.

Another crucial growth driver is the growing emphasis on road safety and security. With the rising number of road accidents and fatalities, there is a pressing need for enhanced safety measures. Smart Transport Systems deploy advanced features such as automated incident detection, real-time alerts, and predictive analytics to prevent accidents and ensure prompt emergency responses. These systems also facilitate the integration of autonomous and connected vehicles, which further enhance road safety through features like collision avoidance and lane-keeping assistance. The increasing investments in autonomous vehicle technology are expected to propel the demand for Smart Transport Systems.

The advent of smart cities and the increasing investments in infrastructure development are also driving the growth of the STS market. Governments across the globe are focusing on developing smart city initiatives to improve the quality of life for their citizens. These initiatives involve the deployment of Smart Transport Systems to enhance urban mobility, reduce environmental impact, and optimize resource utilization. The integration of STS with other smart city components, such as smart grids and smart buildings, creates a cohesive ecosystem that supports sustainable urban development.

The Intelligent Transportation Management System is a pivotal component in the evolution of smart transport solutions. By leveraging cutting-edge technologies such as machine learning, big data, and cloud computing, these systems provide a comprehensive framework for managing and optimizing urban transportation networks. They facilitate seamless integration and communication between various transportation modes, ensuring a cohesive and efficient operation. Moreover, Intelligent Transportation Management Systems are designed to adapt to the dynamic nature of urban environments, offering real-time data analytics and predictive insights that help in proactive decision-making. This adaptability is crucial in addressing the ever-changing demands of urban mobility, making these systems indispensable for future-ready cities.

From a regional perspective, Asia Pacific is expected to witness significant growth in the Smart Transport System market. The region's rapid urbanization, coupled with the increasing investments in smart city projects, is driving the demand for advanced transportation solutions. Countries like China, Japan, and India are leading the way with substantial investments in smart transport infrastructure. Additionally, North America and Europe are also expected to maintain a strong market presence, driven by technological advancements and supportive government policies promoting smart transportation initiatives.

Component Analysis

The Smart Transport System market can be segmented by components into hardware, software, and services. The hardware segment includes various physical devices and infrastructure such as sensors, cameras, traffic signals, and communication systems. These hardware components form the backbone of Smart Transport Systems, enabling real-time data collection, monitoring, and control. The increasing adoption of IoT devices and advancements in sensor technologies are driving the growth of the hardware segment.

The software segment enc

The global Passenger Security System market is experiencing robust growth, projected to reach $6903.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.2% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing concerns about passenger safety and security in public transportation, coupled with stricter government regulations and rising terrorist threats, are compelling transportation authorities and operators to invest heavily in advanced security solutions. Technological advancements, such as the integration of AI and machine learning in CCTV systems for improved threat detection and real-time monitoring, are further boosting market growth. The rising adoption of integrated security platforms offering combined CCTV surveillance, emergency communication systems, and passenger help points also contribute significantly. Different transportation modes, including aircraft, trains, and buses, present unique security challenges, leading to varied system deployments and driving market segmentation. The market segmentation reveals a dynamic landscape. The Recorded CCTV System segment currently holds a significant market share due to its cost-effectiveness and data retention capabilities. However, the Real-time CCTV System segment is witnessing rapid growth, driven by the need for immediate threat response and proactive security measures. Geographically, North America and Europe currently dominate the market due to high levels of technological adoption and stringent safety regulations. However, Asia-Pacific is expected to experience the fastest growth rate in the forecast period due to increasing urbanization, rising passenger traffic, and infrastructure development. While the market faces challenges such as high initial investment costs for advanced systems and concerns about data privacy, the overall growth trajectory remains positive, driven by the undeniable need for enhanced passenger security across all transportation sectors. Companies like Nomad Digital, Bruker, Arrow Security, Bosch, ICTS Europe, Leidos, and L3 Security & Detection Systems are major players shaping innovation and competition in this vital market.