Passenger waiting time prediction plays a critical role in enhancing both ridesharing user experience and platform efficiency. While most existing research focuses on post-request waiting time prediction with knowing the matched driver information, pre-request waiting time prediction (i.e., before submitting a ride request and without matching a driver) is also important, as it enables passengers to plan their trips more effectively and enhance the experience of both passengers and drivers. However, it has not been fully studied by existing works. In this paper, we take the first step toward understanding the predictability and explainability of pre-request passenger waiting time in ridesharing systems. Particularly, we conduct an in-depth data-driven study to investigate the impact of demand&supply dynamics on passenger waiting time. Based on this analysis and feature engineering, we propose FiXGBoost, a novel feature interaction-based XGBoost model designed to predict waiting time without knowing the assigned driver information. We further perform an importance analysis to quantify the contribution of each factor. Experiments on a large-scale real-world ridesharing dataset including over 30 million trip records show that our FiXGBoost can achieve a good performance for pre-request passenger waiting time prediction with high explainability.

Parking in large metropolitan areas is often a time-consuming task with further implications toward traffic patterns that affect urban landscaping. Reducing the premium space needed for parking has led to the development of automated mechanical parking systems. Compared to regular garages having one or two rows of vehicles in each island, automated garages can have multiple rows of vehicles stacked together to support higher parking demands. Although this multi-row layout reduces parking space, it makes the parking and retrieval more complicated. In this work, we propose an automated garage design that supports near 100% parking density. Modeling the problem of parking and retrieving multiple vehicles as a special class of multi-robot path planning problem, we propose associated algorithms for handling all common operations of the automated garage, including (1) optimal algorithm and near-optimal methods that find feasible and efficient solutions for simultaneous parking/retrieval and (2) a novel shuffling mechanism to rearrange vehicles to facilitate scheduled retrieval at rush hours. We conduct thorough simulation studies showing the proposed methods are promising for large and high-density real-world parking applications.

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

We pose the car accident risk prediction as a classification problem with two labels (accident and no accident). It could equally be posed as a regression problem (number of accidents), but on our timescale (one hour) we don't expect to see more than one accident per road segment so this simplifies the problem a bit. There are of course other approaches, but this is the one we take here. Commonly traffic is modeled by a Poisson or Negative binomial model. We can use the seven years and roughly half a million car accident records as our positive examples.

He enrolled in the NYC Data Science Academy 12-week full time Data Science Bootcamp program taking place between July 5th to September 23rd, 2016. This post is based on their second project - R Shiny, due on 4th week of the program. The original article can be found here. Google, Tesla, and other automakers such as BMW, Daimler-Mercedes, and General Motors are all presenting visions of a future where most or all of the responsibilities and tasks of driving are no longer yours. A big benefit of automated driving will be an anticipated reduction in fatal motor vehicle accidents.

Many websites paint a very positive picture of the robocar future. And it is positive, but far from perfect. One problem I worry about in the short term is the way robocars are going to make traffic worse before they get a chance to make it better. The goal of all robocars is to make car travel more pleasant and convenient, and eventually cheaper. You can't make something better and cheaper without increasing demand for it, and that means more traffic.

Thirty of the world's top scientists are scheduled to meet at UC San Diego in February to discuss the toughest challenges in robotics and automation, including how to make driverless cars safe for a mass audience. The experts are being brought together by Henrik Christensen, the prominent Georgia Tech engineer who was hired in July to run UC San Diego's young Contextual Robotics Institute. Christensen said at the time, "I want to build a research institute that, ideally, will be in the top five in the world five years from now. Why not see if we can make San Diego'Robot Valley.'" The February forum is being eyed as a step toward raising the university's visibility in robotics, a field defined by grand advances and embarrassing setbacks.

As we pulled back into the showroom (or whatever Texas' insane dealership protection laws demand Tesla call the places it's not allowed to sell or service vehicles), I told the rep that I was driving to Austin soon; Autopilot would be just the thing for the long stretches of empty road out on I-10 and TX-71. Without missing a beat, she offered me a loaner Model S. Ars has officially driven a Model S with autopilot before, but only under controlled circumstances. The Austin trip would let me take the car out for nearly four hundred miles of driving in a big mix of traffic scenarios. Plus, I'd get to log more cockpit time in a Tesla. Of course I said yes.

The words "machine learning" might conjure images of hyper-intelligent robots in the distant future, but the technology is a reality now. Algorithms powered by machine learning and drawing on big data enable us to solve tough problems that improve people's daily lives. Let's start, for instance, with something many people rely on every day: driving directions. It's pretty well known that our navigation apps rely on GPS satellites to pinpoint our location and complex algorithms to figure out the most direct route to get wherever we want to go. Most of the time, this works just fine.

Bike-sharing systems are becoming increasingly prevalent in urban environments. They provide a low-cost, environmentally-friendly transportation alternative for cities. The management of these systems gives rise to many optimization problems. Chief among these problems is the issue of bicycle rebalancing. Users imbalance the system by creating demand in an asymmetric pattern. This necessitates action to put the system back in balance with the requisite levels of bicycles at each station to facilitate future use. In this paper, we tackle the problem of maintaing system balance during peak rush-hour usageas well as rebalancing overnight to prepare the systemfor rush-hour usage. We provide novel problem formulationsthat have been motivated by both a close collaborationwith the New York City bike share (Citibike) and a careful analysisof system usage data. We analyze system data to discover the best placement of bikes tofacilitate usage. We solve routing problems forovernight shifts as well as clustering problems for handlingmid rush-hour usage. The tools developed from this research are currently in daily use at NYC Bike Share LLC, operators of Citibike.