Employing Digital Crowdsourced Data for Smart Mobility
“With commute times constantly increasing and transit passes becoming prohibitively expensive, transportation is one of the biggest barrier to social mobility. Lower-income Americans spend almost quarter of the annual income on daily commutes, compared to higher-income Americans who spend about one tenth. Creating more roads, bus stops or metro lines is not always an available solution, since many cities are already struggling to maintain the existing ones.” (PSFK)
Drive to the train station, take the train to the next town, and then take the subway to the destination; this is the daily commuting routine for many people living in metropolitan. For the trips between home and work, commuters usually need to take several means of transportation, or travel modes. The objectives to ensure a continuous and seamless mobility in the travel chain provides various challenges to the transportation service providers. Crowdsourced data and the innovative data analytics technologies can help transportation agencies optimize their demand forecasting and service planning.
To improve mobility, it is crucial to optimize the transportation network and the infrastructure locations. The transportation service providers have been trying to identify bottlenecks in the network and provide real-time traveller information for commuter to avoid traffic jams or choose their preferred means of travel. Transportation service providers also need to choose the best locations for infrastructure such as:
- Where are charging stations for electric cars urgently needed?
- Which region is particularly high demand for car-sharing services?
- At what time additional capacity in transportation are needed?
These questions may be answered by the collected crowdsourced data or may be answered through publicly available information from forums, blogs or social media.
To optimize mobility, service providers need to look at the transportation system as a whole. The transportation system has certain capacity; for example, how many vehicles can drive per lane per hour, how many people can accommodate in a bus, streetcar or subway train. How to use these available resources and infrastructure more effectively is the key to optimize mobility. However, travel patterns or travel behaviours have great variation; it is very difficult to predict the demand – whether for road or transit travels.
“Academic researchers and government programs have made great progress using mobile data in developing innovative analytics and metrics to monitor traffic and measure system effectiveness. Some programs have taken this even further by developing new approaches to integrate mobile data into their transportation planning exercises.”(Jason Li)
An additional challenge for the mobility is in short-term unexpected events, such as accidents or severe weather, which lead to service interruption or congestion. A rapid response to these incidents is difficult. The crowdsourced data can “helps transportation authorities manage day-to-day traffic operations, respond to incidents and provide information to transportation system users which optimizes the total system performance and reduces traffic jams” (Jason Li), thus, to make our mobility smarter.
Overall, crowdsourced data would benefit all elements in the transportation system. Although the use of crowsourced data may generate data security and data protection concerns, if they are properly used, the information and knowledge in the high-quality data can help mobility become smarter.
