Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The GPS data set catalogs the vehicle operation data of the test vehicles that used for the MMITSS field testing. The data contains the performance and operation details of vehicles. This file contains a number of fields detailing elements such as vehicle position and speed, fidelity measures of GPS-based data elements, and vehicle operation data. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message

This dataset is the resulting traffic volume data from a recent pilot of computer-vision sensors.

The following metrics were measured: -Volume counts of people, bikes, cars, trucks, and busses passing through pilot project areas -Volumes accounted for by mode and timestamped at up to 15-minute intervals -Desire lines and movement patterns accounted for by mode

The primary purpose of this pilot was to understand the impacts of temporary street-level changes that would be implemented to facilitate a safe re-opening in the context of Covid-19. A secondary objective of the project was to evaluate a privacy-oriented solution to data collection in the public realm.

Sensors were installed at three distinct locations: -In the Seaport district on a commercial street with bike lanes (Northern Avenue) -Downtown at a busy intersection next to the Boston Common (Tremont Street) -And in Jamaica Plain where the southwest corridor convenes with a blue bike station and T stop (Jackson Sq.)

Please complete this form to access the City of Boston Sandbox and access the data in API format: https://docs.google.com/forms/d/e/1FAIpQLScuEQEsmTToEMBRqvX7uhpCiWu165T4GciTCMEa2ylC2bT59w/viewform

Abstract: The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source.

Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation.The Signal Plans for Roadside Equipment (RSE) data contains the basics of a Signal Phase and Timing (SPAT) message. This data includes SPAT message and the timestamp of the SPAT message. The data also provides the signal phase and timing information for one or more movements at an intersection.

The City of Toronto's Transportation Services Division collects short-term traffic count data across the City on an ad-hoc basis to support a variety of safety initiatives and projects. The data available in this repository are a full collection of Turning Movement Counts (TMC) conducted across the City since 1984. The two most common types of short-term traffic counts are Turning Movement Counts and Speed / Volume / Classification Counts. Speed / Volume / Classification Count data, comprised of vehicle speeds and volumes broken down by vehicle type, can be found here. Turning Movement Counts include the movements of motor vehicles, bicycles, and pedestrians through intersections. Counts are captured using video technology. Older counts were conducted manually by field staff. The City of Toronto uses this data to inform signal timing and infrastructure design. Each Turning Movement Count is comprised of data collected over 8 non-continuous hours (before September 2023) or over a continuous 14-hour period (September 2023 and after), at a single location. Some key notes about these counts include: Motor vehicle volumes are available for movements through the intersection (left-turn, right-turn and through-movement for each leg of the intersection). Motor vehicle volumes are further broken down by vehicle type (car, truck, bus). Total bicycle volumes approaching the intersection from each direction are available. Total pedestrian volumes crossing each leg of the intersection are available. Raw data are recorded and aggregated into 15-minute intervals. The following files showing different views of the data are available: Data Dictionary (tmc_data_dictionary.xlsx): Provides a detailed definition of every data field in all files. Summary Data (tmc_summary_data): Provides metadata about every TMC available, including information about the count location and count date, as well as summary data about each count (total 8- or 14-hour pedestrian volumes, total 8- or 14-hour vehicle and bicycle volumes for each approach to the intersection, percent of total that are heavy vehicles and a.m. and p.m. peak hour vehicle and bicycle volumes). Most Recent Count Data (tmc_most_recent_summary_data): Provides metadata about the most recent TMC available at each location for which a TMC exists, including information about the count location and count date, as well as the summary data provided in the “Summary Data” file (see above). Raw Data (tmc_raw_data_yyyy_yyyy): These files—grouped by 5-10 year interval—provide count volumes for cars, trucks, buses, cyclists and pedestrians in 15-minute intervals, for movements through the intersection, for every TMC available. Vehicle volumes are broken down by movement through the intersection (left-turn, right-turn and through-movement, for each approach), cyclist volumes are broken down by leg they enter the intersection and pedestrian volumes are broken down by the leg of the intersection they are counted crossing. This dataset references the City of Toronto's Street Centreline dataset, Intersection File dataset and Street Traffic Signal dataset.

Code and test datasets for the proposed MCGCN model in traffic forecasting.

Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. The Vehicle Trajectories file is populated with basic safety messages received from equipped vehicle within the communication range of an Roadside Equipment (RSEs). The data also contains elements that communicate additional details about the vehicle that is used for vehicle safety applications, and elements that communicate specific items of a vehicle‘s status that are used in data event snapshots which are gathered and periodically reported to an RSEs. These data are transmitted at a rate of 10 Hz. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Message

A Multimodal Thoroughfare Plan is critical for a community as it ensures efficient and sustainable transportation options for its residents. By incorporating various modes of transportation such as walking, cycling, public transit, and private vehicles, this plan addresses the diverse needs of the community while reducing traffic congestion and environmental impacts.

The City of Toronto's Transportation Services Division collects short-term traffic count data across the City on an ad-hoc basis to support a variety of safety initiatives and projects. The data available in this repository are a full collection of Turning Movement Counts (TMC) conducted across the City since 1984. The two most common types of short-term traffic counts are Turning Movement Counts and Speed / Volume / Classification Counts. Speed / Volume / Classification Count data, comprised of vehicle speeds and volumes broken down by vehicle type, can be found here. Turning Movement Counts include the movements of motor vehicles, bicycles, and pedestrians through intersections. Counts are captured using video technology. Older counts were conducted manually by field staff. The City of Toronto uses this data to inform signal timing and infrastructure design. Each Turning Movement Count is comprised of data collected over 8 non-continuous hours (before September 2023) or over a continuous 14-hour period (September 2023 and after), at a single location. Some key notes about these counts include: Motor vehicle volumes are available for movements through the intersection (left-turn, right-turn and through-movement for each leg of the intersection). Motor vehicle volumes are further broken down by vehicle type (car, truck, bus). Total bicycle volumes approaching the intersection from each direction are available. Total pedestrian volumes crossing each leg of the intersection are available. Raw data are recorded and aggregated into 15-minute intervals. The following files showing different views of the data are available: Data Dictionary (tmc_data_dictionary.xlsx): Provides a detailed definition of every data field in all files. Summary Data (tmc_summary_data): Provides metadata about every TMC available, including information about the count location and count date, as well as summary data about each count (total 8- or 14-hour pedestrian volumes, total 8- or 14-hour vehicle and bicycle volumes for each approach to the intersection, percent of total that are heavy vehicles and a.m. and p.m. peak hour vehicle and bicycle volumes). Most Recent Count Data (tmc_most_recent_summary_data): Provides metadata about the most recent TMC available at each location for which a TMC exists, including information about the count location and count date, as well as the summary data provided in the “Summary Data” file (see above). Raw Data (tmc_raw_data_yyyy_yyyy): These files—grouped by 5-10 year interval—provide count volumes for cars, trucks, buses, cyclists and pedestrians in 15-minute intervals, for movements through the intersection, for every TMC available. Vehicle volumes are broken down by movement through the intersection (left-turn, right-turn and through-movement, for each approach), cyclist volumes are broken down by leg they enter the intersection and pedestrian volumes are broken down by the leg of the intersection they are counted crossing. This dataset references the City of Toronto's Street Centreline dataset, Intersection File dataset and Street Traffic Signal dataset.

The Central Netherlands region wants to implement its ambitions to steer on the multimodal networks in the province. Liveability and sustainability as a prerequisite for mobility play an increasingly important role. Instead of optimising the flow only for car traffic (traditional traffic management), traffic management will be organised multimodal in the coming years. This is done by regional cooperation in the Multimodal Traffic Management (MUVM) programme. This is in line with the policy-based desire to stimulate active and collective transport modes above (individual) car traffic.The province of Utrecht strives for a joint starting point on how the different road managers (within and adjacent to the province) deal with these ambitions. The Multimodal Network Framework (MNK) jointly drawn up by the road managers concerned serves in substance to make a well-considered balance between the different modalities and gives an answer to the question of which modalities in which situation should be given additional priority at the expense of (the priority of) one or more other modalities. The framework helps to bring the representatives of the different modalities to the table and to determine in full what is needed to make better use of the overall mobility infrastructure. The MNK is the successor to the steering vision Dynamic Traffic Management Central Netherlands from 2015. This control vision was developed at the time using the methodology of Gebiedsgericht Benutten Plus (GGB+) and focuses only on car traffic. The management vision has been established administratively in the past.

The data attached and/or displayed were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation.

A BSM is one of the messages belonging to the Society of Automotive Engineers (SAE) J2735 Standard. This standard is geared toward supporting the interoperability of DSRC applications through the use of a standardized message set and its data frames and data elements. A BSM, which is at times referred to as a “heartbeat” message, is a frequently transmitted message (usually at approximately 10Hz) that is meant to increase a vehicle’s situational awareness. These messages are intended to be used for a variety of applications to exchange safety data regarding a vehicle’s state.

A typical BSM contains up to two parts. Part I, the binary large object (blob), is included in every BSM. It contains the fundamental data elements that describe a vehicle’s position (latitude, longitude, elevation) and motion (heading, speed, acceleration). Part II of a BSM contains optional data that is transmitted when required or in response to an event. Typically Part II contains data that serves as an extension of vehicle safety information (path history, path prediction, event flags) and data pertaining to the status of a vehicle’s components, such as lights, wipers, and brakes. 

NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Messages such as MAP, Detectors, and Simulation results

The data attached and/or displayed were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to provide a comprehensive traffic information framework to service all modes of transportation. A BSM is one of the messages belonging to the Society of Automotive Engineers (SAE) J2735 Standard. This standard is geared toward supporting the interoperability of DSRC applications through the use of a standardized message set and its data frames and data elements. A BSM, which is at times referred to as a “heartbeat” message, is a frequently transmitted message (usually at approximately 10Hz) that is meant to increase a vehicle’s situational awareness. These messages are intended to be used for a variety of applications to exchange safety data regarding a vehicle’s state. A typical BSM contains up to two parts. Part I, the binary large object (blob), is included in every BSM. It contains the fundamental data elements that describe a vehicle’s position (latitude, longitude, elevation) and motion (heading, speed, acceleration). Part II of a BSM contains optional data that is transmitted when required or in response to an event. Typically Part II contains data that serves as an extension of vehicle safety information (path history, path prediction, event flags) and data pertaining to the status of a vehicle’s components, such as lights, wipers, and brakes. NOTE: All extra attachments are located in Multi-Modal Intelligent Traffic Signal Systems Basic Safety Messages such as MAP, Detectors, and Simulation results

The Central Netherlands region wants to implement its ambitions to steer on the multimodal networks in the province. Liveability and sustainability as a prerequisite for mobility play an increasingly important role. Instead of optimising the flow only for car traffic (traditional traffic management), traffic management will be organised multimodal in the coming years. This is done by regional cooperation in the Multimodal Traffic Management (MUVM) programme. This is in line with the policy-based desire to stimulate active and collective transport modes above (individual) car traffic.The province of Utrecht strives for a joint starting point on how the different road managers (within and adjacent to the province) deal with these ambitions. The Multimodal Network Framework (MNK) jointly drawn up by the road managers concerned serves in substance to make a well-considered balance between the different modalities and gives an answer to the question of which modalities in which situation should be given additional priority at the expense of (the priority of) one or more other modalities. The framework helps to bring the representatives of the different modalities to the table, and to determine in full what is needed to make better use of the overall mobility infrastructure.The MNK is the successor of the steering vision Dynamic Traffic Management Central Netherlands from 2015. This control vision was developed at the time using the methodology of Gebiedsgericht Benutten Plus (GGB+) and focuses only on car traffic. The management vision has been established administratively in the past.

Locations of historic traffic calming initiatives from 2006 onward.Sources: Sustainability traffic calming locations Web Map by mhebert Created: Aug 28, 2018 Updated: Aug 30, 2018 View Count: 152Map URL: https://arcg.is/1WjuT4

Updated quarterly

Provisional data on road traffic by vehicle type and road class, produced by Department for Transport.

** Static data set describing trajectories related to multimodal counting sites (Bike, Trottinette, 2RM, VL, PL, Bus-car).** The City of Paris collects vehicle counting data by: * travel modes (Trottinettes, Trottinettes + Bicycles (when the distinction between these two modes of travel is not implemented on the sensor), Bikes, 2 motorised wheels, Light vehicles ‘3.5 tonnes, Heavy vehicles’ 3.5 tonnes, Buses & coaches), * traffic queue types (Corona-piste, Bicycle paths, General traffic routes), * traffic direction. This data is built using a artificial intelligence algorithm that analyses images from thermal cameras installed in public space. ** Images from thermal cameras do not identify faces or license plates. The data thus collected does not present personal or individual data.** No image is transferred or stored on computer servers, the analysis being carried out as close as possible to the thermal camera. Only counting data is transmitted. This dataset feeds into the counting dataset ****** Multimodal counting – Counts** Accuracy on the content of the “Trajectory” field: It is a string designating the detection start area (input) and the detection output zone (Enter > Output) A trajectory is characterised by a direction of circulation and by the traffic line taken by the vehicle entering and exiting. Example: A bike that can in the detection area of the thermal camera enter the bike path and exit the general traffic lane.

Accessibility Observatory data reflects the number of jobs that are reachable by various modes within different travel times from different Census-defined geographies in Massachusetts (block, block group, tract). The data comes from the Accessibility Observatory at the University of Minnesota, and the underlying jobs data is sourced from the U.S. Census Bureau’s Local Employer Household Dynamics (LEHD) dataset. More information about data methodology is available here:https://access.umn.edu/publications/.The data posted on GeoDOT is initially organized by mode: Auto, Transit, Pedestrian, and Bike. With respect to Auto, Transit, and Pedestrian data, data is then organized by geography (group and block group), and then travel time threshold: 30, 45, and 60 minutes. Please note that MassDOT has access to data that reflects travel time thresholds in five minute increments, email Derek Krevat at derek.krevat@dot.state.ma.us for more information. With respect to Bike data, data is organized by geography (group and block group) and then by Level of Traffic Stress; there are four different levels that correspond to the ratings given different roadway segments with respect to the level of 'traffic stress' imposed on cyclists LTS 1: Strong separation from all except low speed, low volume traffic. Simple crossings. Suitable for children. LTS 2: Except in low speed / low volume traffic situations, cyclists have their own place to ride that keeps them from having to interact with traffic except at formal crossings. Physical separation from higher speed and multilane traffic. Crossings that are easy for an adult to negotiate. Corresponds to design criteria for Dutch bicycle route facilities. A level of traffic stress that most adults can tolerate, particularly those sometimes classified as “interested but concerned.”LTS 3: Involves interaction with moderate speed or multilane traffic, or close proximity to higher speed traffic. A level of traffic stress acceptable to those classified as “enthused and confident.”LTS 4: Involves interaction with higher speed traffic or close proximity to high speed traffic. A level of stress acceptable only to those classified as “strong and fearless.” Seehttps://www.northeastern.edu/peter.furth/research/level-of-traffic-stress/for more information.· Data reflecting access to jobs via Auto is available for each hour of the day at the different travel time thresholds (30, 45 and 60 minute thresholds are posted; five minute thresholds are available by contacting Derek Krevat at derek.krevat@dot.state.ma.us).o For convenience, MassDOT has also created stand-alone summary files that reflect the total number of jobs available throughout the day within 30, 45, and 60 minutes of travel time. See the Data Dictionary, Auto All Jobs for more information.· Pedestrian and Transit data is only available for the morning peak travel period, 7:00 to 9:00 am.· Bicycle data is only available for the noontime hour.· Each of the data files contains data reflecting access to all jobs as well as discrete job opportunities as categorized by the U.S. Census bureau, such as jobs in specific industries, with specific types of workers, with specific wages, or in businesses of certain sizes or ages. See the Data Dictionary for more information.

Comparison of highway abnormal event detection models.

Location of identified multimodal goods platforms in the territory. A multimodal platform refers to the place where the goods change mode of transport. It must ensure the best conditions for intermodal transport and the combined transport of goods. A multimodal platform also allows the consolidation and redistribution of freight (or freight) traffic. It can be a port, a freight station, an airport... connected to another transport network. The ports of goods in the region are thus represented in this table. Origin of the data: Data from IGN Topo® BD

Set of multimodal hourly counting data by displacement mode from thermal sensors. The City of Paris collects vehicle counting data by: * travel modes (Trottinettes, Trottinettes + Bicycles (when the distinction between these two modes of travel is not implemented on the sensor), Bikes, 2 motorised wheels, Light vehicles ‘3.5 tonnes, Heavy vehicles’ 3.5 tonnes, Buses & coaches), * traffic queue types (Corona-piste, Bicycle paths, General traffic routes), * traffic direction. This data is built using a artificial intelligence algorithm that analyses images from thermal cameras installed in public space. ** Images from thermal cameras do not identify faces or license plates. The data thus collected does not present personal or individual data.** No image is transferred or stored on computer servers, the analysis being carried out as close as possible to the thermal camera. Only counting data is transmitted. This dataset is powered by the metering performed by the sensors and the dataset describing the trajectories of the counting sites ** Multimodal counting – Counting Sites and Trajectories** The number of sensors and their ability to distinguish the type of vehicles (e.g. scooters and bicycles) can change over time. ** ** Accuracy on the content of the “Trajectory” field: It is a string designating the detection start area (input) and the detection output zone (Enter > Output) A trajectory is characterised by a direction of circulation and by the traffic line taken by the vehicle entering and exiting. Example: A bike that can in the detection area of the thermal camera enter the bike path and exit the general traffic lane. More details can be found in the package leaflet of the dataset.