LSTW is a large-scale, country-wide dataset for transportation and traffic research, which contains traffic and weather event data for the United States. In terms of traffic, we have several types of events including accident, congestion, construction, etc. In terms of weather events, we have several types including rain, snow, storm, cold weather event, etc. This dataset is continuously being collected from August 2016. Please read below descriptions for further details on this dataset.
Frequency distribution of traffic events in Bay Area from August 2016 to June 2019 using Kepler.gl
Please cite the following paper if you use this dataset:
- Moosavi, Sobhan, Mohammad Hossein Samavatian, Arnab Nandi, Srinivasan Parthasarathy, and Rajiv Ramnath. “Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data.” In proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, 2019.
Usage Policy and Legal Disclaimer
This dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may cite the above paper if you use this dataset.
- Version 2 (June 2019): In this version, we provide data which is collected from August 2016 to the end of June 2019 for the Contiguous United States. In this set, we have about 21.3 million traffic and 3.8 million weather event records. Download data from here.
The next version will be available by December 2019.
The data is provided in terms of a single CSV file. Following table describes the data attributes:
|1||EventId||This is the identifier of a record||No|
|2||Source||Indicates the source of an event which is either T (traffic) or W (weather).||No|
|3||Type||The type of an event; examples are rain, snow, accident, etc.||No|
|4||Severity||The severity of an event, wherever applicable. For a traffic event, severity is a number between 1 and 4, where 1 indicates the least impact on traffic (i.e., short delay as a result of the event) and 4 indicates a significant impact on traffic (i.e., long delay).||Yes|
|5||TMC||Each traffic event has a Traffic Message Channel (TMC) code which provides a more detailed description on type of the event.||Yes|
|6||Description||The natural language description of an event (only for traffic event).||Yes|
|7||StartTime (UTC)||The start time of an event in UTC time zone.||No|
|8||EndTime (UTC)||The end time of an event in UTC time zone.||No|
|9||TimeZone||The US-based timezone based on the location of an event (eastern, central, mountain, and pacific).||No|
|10||LocationLat||The latitude in GPS coordinate.||Yes|
|11||LocationLng||The longitude in GPS coordinate.||Yes|
|12||Distance (mi)||The length of the road extent affected by the event (only for traffic event).||Yes|
|13||AirportCode||The airport station that a weather event is reported from, or the closest airport station to the location of a traffic event.||Yes|
|14||Number||The street number in address record (only for traffic event).||Yes|
|15||Street||The street name in address record (only for traffic event).||Yes|
|16||Side||The relative side of a street (R/L) in address record (only for traffic event).||Yes|
|17||City||The city in address record.||Yes|
|18||County||The county in address record.||Yes|
|19||State||The state in address record.||Yes|
|20||ZipCode||The zipcode in address record.||Yes|
Description of Traffic Event Types
Traffic event is a spatiotemporal entity, where such entity is associated with location and time. Following is the description of available traffic event types:
- Accident: Refers to traffic accident which can involve one or more vehicles.
- Broken-Vehicle: Refers to the situation when there is one (or more) disabled vehicle(s) in a road.
- Congestion: Refers to the situation when the speed of traffic is lower than the expected speed.
- Construction: Refers to an on-going construction, maintenance, or re-paring project in a road.
- Event: Refers to the situations such as sport event, concert, and demonstration.
- Lane-Blocked: Refers to the cases when we have blocked lane(s) due to traffic or weather condition.
- Flow-Incident: Refers to all other types of traffic events. Examples are broken traffic light and animal in the road.
Description of Weather Event Types
Weather event is a spatiotemporal entity, where such entity is associated with location and time. Following is the description of available traffic event types:
- Severe-Cold: The case of having extremely low temperature, with temperature below -23.7 degrees of Celsius.
- Fog: The case where there is low visibility condition as result of fog or haze.
- Hail: The case of having solid precipitation including ice pallets and hail.
- Rain: The case of having rain, ranging from light to heavy.
- Snow: The case of having snow, ranging from light to heavy.
- Storm: The extremely windy condition, where the wind speed is at least 60 kmh.
- Other Precipitation: Any other type of precipitation which cannot be assigned to previously described event types.
Visit our paper to learn how we determine type and severity of weather events.
The data coverage is country-wide. It currently contains data for the Contiguous United States. Following diagram shows the current frequency distribution of traffic events across 50 difference states in US.
Note: For the following states, we have no traffic data from August 2016 to August 2017: AL, AR, AZ, CO, ID, KS, KY, LA, ME, MN, MS, MT, NC, ND, NH, NM, NV, OK, OR, SD, TN, UT, VT, WI, and WY.
The next diagram shows the current frequency distribution of weather events for the same set of states.
Following diagram shows the current distribution of different traffic event types:
Similarly, current distribution of weather event types is shown in below diagram:
Applications of Dataset
This dataset can be used for plenty of purposes such as traffic analysis and prediction, impact prediction, accident prediction, routing engine optimization, travel time estimation, and many other research applications.