Demand estimation and chance-constrained fleet management for ride hailing

In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly provided as it would be with an app, but rather customers are only served if a vehicle happens to be located at the arrival location. This work focuses on maximizing the number of served hailing customers in an MOD system by learning and utilizing customer demand. A Bayesian framework is used to define a novel customer demand model which incorporates observed pedestrian traffic to estimate customer arrival locations with a quantification of uncertainty. An exploration planner is proposed which routes MOD vehicles in order to reduce arrival rate uncertainty. A robust ride hailing fleet management planner is proposed which routes vehicles under the presence of uncertainty using a chance-constrained formulation. Simulation of a real-world MOD system on MIT's campus demonstrates the effectiveness of the planners. The customer demand model and exploration planner are demonstrated to reduce estimation error over time and the ride hailing planner is shown to improve the fraction of served customers in the system by 73% over a baseline exploration approach.Ford-MIT AllianceFord Motor Compan

Evaluation of a Pedestrian Call Extension and Cancelation System at a Midblock Trail Crossing

Signalized intersections often provide pushbuttons to call for pedestrian service to minimize conflicting movements between road users. Signalized midblock crossings typically increase safety for pedestrians in locations with high traffic volumes and multiple lanes in each direction. However, unnecessary vehicular delay occurs from “ghost” pedestrian intervals when someone activates the pedestrian phase and then jaywalks. Safety issues also arise when pedestrians remain in the crosswalk at the end of the Flashing Don’t Walk phase, and vehicles receive the circular green. A Pedestrian Call Extension and Cancelation System was evaluated at a signalized midblock trail crossing in Hillsboro, OR. Thermal sensors and new traffic controller logic were implemented to detect pedestrian locations and passively cancel or extend calls. A longitudinal before-after study was conducted to evaluate how vehicular delay and pedestrian behavior was impacted with installation of the system. Reasons for cancelations and extensions were also documented. A total of 1,649 observations before system installation and 3,002 observations after were recorded. Vehicular delay decreased significantly, and most cancelations were determined to be caused by jaywalkers. Considerations for installation in different contexts are discussed

The Triangle Metric: Fast Link Quality Estimation for Mobile Wireless Sensor Networks (Invited Paper)

ReSensesuppleCONE

Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations

The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City

Feasibility of using wearable devices for collecting pedestrian travel data

Information on the travel characteristics of pedestrians is needed in the planning and design of pedestrian facilities. Desired information includes route selected, travel speed, trip origin and destination, and delay. Conventional methods of acquiring pedestrian travel data such as trip diaries suffer from a number of limitations.;Pedometers are simple wearable devices that are receiving considerable attention in the health promotion and physical activity fields. In recent years, there have been significant developments in global positioning system (GPS) technology. User-friendly devices are now available for under {dollar}100. At the same time, more expensive wearable GPS data loggers are available in the market that are capable of collecting more extensive data. While the technology offers great potential in terms of data collection capabilities, questions about accuracy, reliability, user acceptability, and post-processing requirements must be addressed.;A formal assessment was conducted of pedometers, a hand-held GPS unit and a wearable data logger to determine their feasibility in collecting pedestrian travel data. Experiments were devised and conducted to assess the accuracy and reliability of the devices in a variety of conditions including heavy precipitation, dense vegetative cover and between tall buildings. In addition, devices were given to a number of subjects who used them outdoors for a 24-hour period. Each subject also completed a brief questionnaire intended to assess user acceptability of these devices. Results indicated that the pedometer is not suitable for collecting pedestrian travel data. The GPS devices hold promise as data collection devices as long as their limitations are taken into account. The paper presents recommendations about the suitability of each device for collecting pedestrian travel data

Transit Demand Estimation And Crowding Prediction Based On Real-Time Transit Data

With an increasing number of intelligent analytic techniques and increasing networking capabilities, municipal transit authorities can leverage real-time data to estimate transit volume and predict crowding conditions. We introduce a proactive Transit Demand Estimation and Prediction System (TraDEPS) – an approach that has the potential to prevent crowding and improve transit service, by measuring the transit activity (the number of passengers on the individual modes of public transportation and the demand on a route), and estimating crowding levels at a given time. This system utilizes a combination of real-time data streams from multiple sources, a predictive model and data analytics for transit management. The problem of transit crowding is translated into transit activity prediction, as the latter is a straightforward indicator of the former. This thesis delivers the following contributions: (1) A crowding prediction model. (2) A system supporting the methodology. (3) A feature which displays different crowding level conditions of a route on a web map