Back in those dark ages, Uber isn\u2019t yet a thing. You need to rush all the way up the escalators, jump into the middle of the road to stop a passing taxi in its tracks, gesture deliriously that you need to get to the airport, and pray that there are no traffic jams on the way. Because you\u2019re already late.<\/p>\n
Today, your average traveler is far more connected thanks to the ubiquity of real-time big data in transportation. You know exactly when to leave, which mode of transportation to take, and what your contingency plan is if there\u2019s a strike, a blizzard, an alien invasion, or any other type of traffic disruption.<\/p>\n
Yet as cities grow denser and more crowded with private, public, and shared vehicles, managing the entire conundrum becomes not only harder but critical.<\/p>\n
Time to shift gears: How will big data change the future of transportation?<\/h2>\n
According to the United Nations<\/a>, there are 37 megacities today \u2014 dense metro areas with a population of 10+ million. By 2030, that figure is projected to increase to 47.<\/p>\n This highly concentrated form of urban dwelling we are shifting towards poses a host of challenges including resource supplies, waste management, and rising inequality.<\/p>\n But arguably the biggest issue (and at the same time, solution) is transportation management.<\/p>\n City congestion levels and population density<\/b> Source: Accenture \u2014 Society disrupted, now what?<\/a><\/em><\/p>\n In addition to people, world cities house a growing array of mobility players:<\/p>\n All of them navigate a city\u2019s arteries at different capacities, with fluctuating demand, under varying weather conditions. Such ubiquity brings new dilemmas:<\/p>\n Given the rapid growth in emerging mobility sectors<\/a> such as ACES vehicles (autonomous, connected, electric, shared) and micro-mobility solutions, we cannot afford to leave those questions unanswered.<\/p>\n And this means we need to reconcile big data and transportation management.<\/p>\n There\u2019s no shortage of big data in logistics, transportation, and urban planning. But that raw intel is rarely integrated into transportation planning activities, or is used only to a limited extent.<\/p>\n So how does big data affect the transportation industry at the moment? What can we do to put it to better use in the future? Buckle up and let\u2019s go on a drive around the block.<\/p>\n Transportation is complex because you need to orchestrate a cacophony of travel patterns into a coherent symphony of neat traffic flows \u2014 a task even Mozart would dread.<\/p>\n But the good news is that you get to use your instruments. And boy are there plenty:<\/p>\n Orchestrating a fine-sounding transport management<\/a> scenario becomes a matter of selecting the optimal big data transportation use cases. We\u2019ve got several lined up.<\/p>\n Transportation and logistics development<\/p>\n If you\u2019ve ever lived in a big city, you know the morning drill: leave at 7:30 to beat the traffic or take the 8:30 train if you overslept. If it rains, add an extra 15 to 20 minutes to your commute. If there\u2019s a blizzard… well, maybe you should work from home.<\/p>\n Personally, each of us makes predictions about road traffic conditions. But professionally, why estimate traffic demand on a hunch when you can use historical big data for transportation planning<\/a> and churn out a multitude of accurate predictions<\/a> in a matter of minutes?<\/p>\n Here are the types of big data sources you can leverage for demand forecasting and transport planning:<\/p>\n The best part? You don\u2019t even need to use all of these transportation data analytics sources to get accurate predictions.<\/p>\n A group of Chinese researchers<\/a> used call detail record data only to accurately map the physical travel patterns of residents in a city of 6 million. The team relied on a data fusion technique to form a labeled data set for supervised statistical learning. Then they used logic regression, artificial neural networks, and a support vector machine to create statistical classification models, producing on-target forecasts.<\/p>\n Traffic jams are the bane of many city dwellers and a locus of profound pressure for city managers.<\/p>\n Road premiums, public transport discounts, narrower one-way lanes, tunnels, \u201cnaked\u201d streets without traffic lights, even flying cars and drone-based logistics<\/a> \u2014 there\u2019s no shortage of realistic and aspirational ideas for combatting traffic congestion. Some work to an extent. Others flop. Yet no city is completely free of traffic jams<\/a>.<\/p>\n How come? Because no two urban layouts are alike. Traffic congestion may bear painful similarities across the globe, but the root causes differ.<\/p>\n Some locations are more prone to congestion-inducing weather conditions \u2014 rain, fog, snow, hail, and other natural occurrences are the main culprit of an estimated 15% of traffic congestion cases<\/a> in the US.<\/p>\n Suboptimal physical infrastructure design leads to recurring congestion and continuous bottlenecks at popular locations. But reducing the hourly throughput at such locales is either not feasible or too costly to implement.<\/p>\n While it can\u2019t fix physical causes, you can optimize the digital end of this conundrum with a big data-fueled intelligent transportation system or more targeted traffic optimization solutions\u0411<\/a>:<\/p>\n Technologically, all of the above use cases of big data in transportation and traffic engineering are already feasible.<\/p>\n Learn about our experience building an IoT traffic management solution<\/p>\n Did you know that we\u2019re wired to tolerate a one-hour commute per day maximum (roughly 30 minutes each way)?<\/p>\n Better known as Marchetti\u2019s Constant<\/a>, this 30-minute travel time has been shaping city layouts and dwelling patterns for centuries (and our tolerance for paying exorbitant rent if it means a shorter commute).<\/p>\n However, rising urban density and subsequent traffic congestion undermines our evolutionary inclination to stay within the 30-minute mark. Some cities choose to double down on public transport network development<\/a> and impose various restrictions to make personal car use an expensive choice, which can be a viable long-term solution.<\/p>\n But commute times need fixing today. A combination of big data and analytics for intelligent transportation systems<\/a> can provide immediate relief. By operationalizing available sources, you can:<\/p>\n Check out a detailed case study about building an IoT-powered mobility as a service solution<\/p>\n Singapore, a city consistently praised for the best transport system, designed a transportation route layout where 90% of the population<\/a> lives within 300 meters of a bus stop. Next, they effectively integrated the bus network with the city\u2019s metro and light rail systems to promote multi-modal journeys<\/a>. These accounted for 67% of all journeys as of 2018.<\/p>\n Now, Singaporean officials are tackling road traffic management<\/a>. In 2017, they rolled out a transportation data analysis system capable of aggregating and analyzing real-time road traffic data using Global Navigation Satellite System (GNSS) technology and in-vehicle telematics data.<\/p>\n By mid-2023<\/a>, the city-state plans to switch to a satellite-based electronic road pricing (ERP) system for collecting road tolls and dynamically managing road prices. An onboard vehicle unit will provide drivers with real-time information on ERP charging locations and rates, overall traffic conditions, and traffic zones with special speed restrictions.<\/p>\n Intellias too has been working on the future of commute optimization as a technology partner for an automotive company. We\u2019ve been helping our client establish low-latency processing of geospatial data<\/a> and deploy custom geolocation solutions to the market that provide urban planners with precise operational insights.<\/p>\n Most every driver has received a parking fine on the windshield. But the truth is that parking tickets are not the real source of frustration \u2014 lack of available spots is.<\/p>\n Insufficient parking also generates extra costs for municipalities:<\/p>\n On the other hand, overparking also has negative implications on housing availability, plus it reinforces car dependence.<\/p>\n
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Big data in transportation: Start your analytics engines with proven use cases<\/h2>\n
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Big data analytics for transportation demand forecasting<\/h3>\n
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Traffic congestion management<\/h3>\n
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Commute optimization<\/h3>\n
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Parking management<\/h3>\n
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