Modeling Framework and Solution Methodologies for On-Demand Mobility Services With Ridesharing and Transfer Options

The growing complexity of the urban travel pattern and its related traffic congestion, along with the extensive usage of mobile phones, invigorated On-Demand Mobility Services (ODMS) and opened the door to the emergence of Transportation Network Companies (TNC). By adopting the shared economy paradigm, TNCs enable private car owners to provide transportation services to passengers by providing user-friendly mobile phone applications that efficiently match passengers to service providers. Considering the high level of flexibility, convenience, and reliability of ODMS, compared to those offered by traditional public transportation systems, many metropolitan areas in the United States and abroad have reported rapid growth of such services. This dissertation presents a modeling framework to study the operation of on-demand mobility services (ODMS) in urban areas. The framework can analyze the operation of ODMS while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed-integer program and an efficient decomposition-based methodology is developed for its solution. This solution methodology aims at solving the offline version of the problem, in which the passengers’ demand is assumed to be known ii for the entire planning horizon. The presented approach adopts a modified column generation algorithm, which integrates iterative decomposition and network augmentation techniques to analyze networks with moderate size. Besides, a novel methodology for integrated ride-matching and vehicle routing for dynamic (online) ODMS with ridesharing and transfer options is developed to solve the problem in real-time. The methodology adopts a hybrid heuristic approach, which enables solving large problem instances in near real-time, where the passengers’ demand is not known a priori. The heuristic allows to (1) promptly respond to individual ride requests and (2) periodically re-evaluate the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The outcomes of experiments considering hypothetical and real-world networks are presented. The results show that the modified column generation approach provides a good quality solution in less computation time than the CPLEX solver. Additionally, the heuristic approach can provide an efficient solution for large networks while satisfying the real-time execution requirements. Additionally, investigation of the results of the experiments shows that increasing the number of passengers willing to rideshare and/or transfer increases the general performance of ODMS by increasing the number of served passengers and associated revenue and reducing the number of needed vehicles

Congestion based Truck Drone intermodal delivery optimization

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage

Modeling and Simulation of Vehicle to Vehicle and Infrastructure Communication in Realistic Large Scale Urban Area

During the last decades, Intelligent Transportation System (ITS) has progressed at a rapid rate, which aim to improve transportation activities in terms of safety and efficiency. Car to Car or Vehicle-to-Vehicle (V2V) communications and Car/Vehicle-to-Infrastructure (I2V or V2I) communications are important components of the ITS architecture. Communication between cars is often referred to Vehicular Ad-Hoc Networks (VANET) and it has many advantages such as: reducing cars accidents, minimizing the traffic jam, reducing fuel consumption and emissions and etc. VANET architectures have been standardized in the IEEE-802.11p specification. For a closer look on V2V and V2I studies, the necessity of simulations is obvious. Network simulators can simulate the ad-hoc network but they cannot simulate the huge traffic of cities. In order to solve this problem, this thesis studies the Veins framework which is used to run a traffic (SUMO) and a network (OMNET++) simulator in parallel and simulates the realistic traffics of the city of Cologne, Germany, as an ad-hoc network. Several different simulations and performance analyses have been done to investigate the ability of different VANET applications. In the simulations, cars move in the real map of the city of Cologne and communicate with each other and also with RoadSideUnits with using IEEE 802.11p standard. Then, Probability of Beacons Delivery (PBD) in different area of a real city are calculated and also are compared with the analytical model. This study is the first research performed on calculating PBD of IEEE 802.11p in realistic large urban area. Then, the thesis focuses on modelling and analysis of the applications of the V2I in real city. In these sections, two different simulations of application of the VANET are done by developing the Veins framework and also by developing two new programs written in Python which are connected to SUMO and control the real traffic simulation. One program simulates a real city with intelligent traffic lights for decreasing response time of emergency vehicles by using V2I. The results show that using V2I communication based on 802.11p between emergency cars and traffic lights can decrease the response time of emergency cars up to 70%. Another program, simulates dynamic route planning in real traffic simulation which is used V2I and V2V communication. The result of this simulation show the capability of V2V and V2I to decrease the traveling time, fuel consumptions and emissions of the cars in the city

A microscopic simulation laboratory for advanced public transportation system evaluation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 178-180).by Daniel J. Morgan.S.M

Modeling vehicle fuel consumption with mobile phone sensor data through a participatory sensing framework

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 106-109).Vehicle energy efficiency has become a priority of governments, researchers, and consumers in the wake of rising fuels costs over the last decade. Traditional Internal Combustion Engine (ICE) vehicles are particularly inefficient on high traffic or urban roadways characterized by stop-and-go driving patters. We have developed a novel regression model named StreetSmart that can serve as a transfer function between 4 traffic classification parameters we call "Energy Indices" and the fuel consumption of specific vehicle makes. In formulating the model, we show that average speed, which is the most common metric used to report traffic, is actually inadequate to quantify the impact of traffic conditions on bulk energy consumption. Rather, we use an analysis of traffic microstructure, which is the detailed acceleration profile of individual vehicles on a road segment. Using data logged on OBD-II and smartphone devices from over 600 miles of driving, we have shown that the model is capable of predicting fuel consumption with an average accuracy of over 96% using regression coefficients obtained from the same vehicle make and similar road types. Mean prediction error for all cases ranged from -2.43% to 0.06% while the max prediction error was 7.85%. We have also developed a framework for the broader StreetSmart System, a participatory sensing network that will be used to crowdsource mass quantities of smartphone accelerometer and GPS data from drivers. We propose a system architecture and discuss problems of distribution, reliability, privacy, and other concerns. Finally, we introduce future applications of StreetSmart, including hybrid vehicle drivetrain power management, electric vehicle range estimation, congestion pricing, and traffic data services.by Austin Louis Oehlerking.S.M

Suitability of distributed mobile wireless networking for urban traffic congestion mitigation

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2001.Includes bibliographical references (leaves 101-103).A suitability study is performed into the use of distributed mobile wireless networking for the purposes of urban traffic congestion mitigation. The technologies of global positioning system (GPS), wireless networking, and mobile ad-hoc networking (MANET) protocols are surveyed for potential usability and applicability in a peer-to-peer highway vehicle network. Analysis of traffic statistics for the Boston, MA metropolitan area reveal the parameters required to build an initial network. The estimated parameters are a two percent level of penetration (50,000 vehicles), two Megabit per second usable data bandwidth, one half mile average transmission range, two hundred dollars cost per device, and fifteen million dollar total system cost for five years of operation. Using a hop-count routing algorithm, the network would support collection of area-wide vehicle positions for automated highway traffic sampling and fleet tracking on congested roadways. Following this first stage system are presented two more application scenarios according to increasing levels of penetration and increased reliability of the network. The medium-term application is the provision of mobile Internet access to allow consumer and business services. The long-term application is the ability to perform automated transactions. Envisioned in this long-term scenario is the ability to do area-wide road pricing to reduce congestion levels and influence land-use decisions. Technology options and design choices for privacy protection are discussed including voluntary participation, incentivized participation, blackout zones, aggregation of data, non-identifiable data, and anonymous routing protocols. Centralized toll tables and transactions are shown to reduce privacy but increase convenience as opposed to distributed toll tables and in-vehicle transactions. Institutional implementation through Federal ITS funding of a State-run public-private partnership is suggested to maximize mutual benefit. Given these options for handling the issues, the staging presented, and the flexibility, coverage, and application benefits of the system, the conclusion is that such a network would be suitable for mitigation of urban traffic congestion.by Shailesh Niranjan Humbad.M.C.P

Opportunistic sensing and mobile data delivery in the CarTel System

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 94-102).Wide-area sensor systems enable a broad class of applications, including the fine-grained monitoring of traffic congestion, road surface conditions, and pollution. This dissertation shows that it is possible to build a low-cost, wide-area sensor system. Our approach relies on two techniques: using existing motion from such sources of mobility as cars and people to provide coverage (opportunistic mobility), and using the abundance of short duration network connections to provide low-cost data delivery (opportunistic networking). We use these two techniques to build a mobile sensor computing system called CarTel, to collect, process, deliver, and visualize spatially diverse data. CarTel consists of three key components: hardware placed in users' cars to provide remote sensing, a communication stack called CafNet to take advantage of opportunistic networking, and a web-based portal for data visualization. This dissertation describes the design and implementation of these three components. In addition, we analyze the properties of opportunistic networking and mobility. To show the viability of opportunistic networking, we studied Internet access from moving vehicles and found that the median duration of link layer connectivity at vehicular speeds was 13 seconds, that the median connection upload bandwidth was 30 KBytes/s, and that the mean duration between successful associations to APs was 75 seconds. To show the viability of opportunistic mobility, we used a simulation and found that after as little as 100 drive hours, a CarTel deployment could achieve over 80 percent coverage of useful roads for a traffic congestion monitoring application.by Bret W. Hull.Ph.D

FRIEND: A Cyber-Physical System for Traffic Flow Related Information Aggregation and Dissemination

The major contribution of this thesis is to lay the theoretical foundations of FRIEND — A cyber-physical system for traffic Flow-Related Information aggrEgatioN and Dissemination. By integrating resources and capabilities at the nexus between the cyber and physical worlds, FRIEND will contribute to aggregating traffic flow data collected by the huge fleet of vehicles on our roads into a comprehensive, near real-time synopsis of traffic flow conditions. We anticipate providing drivers with a meaningful, color-coded, at-a-glance view of flow conditions ahead, alerting them to congested traffic. FRIEND can be used to provide accurate information about traffic flow and can be used to propagate this information. The workhorse of FRIEND is the ubiquitous lane delimiters (a.k.a. cat\u27s eyes) on our roadways that, at the moment, are used simply as dumb reflectors. Our main vision is that by endowing cat\u27s eyes with a modest power source, detection and communication capabilities they will play an important role in collecting, aggregating and disseminating traffic flow conditions to the driving public. We envision the cat\u27s eyes system to be supplemented by road-side units (RSU) deployed at regular intervals (e.g. every kilometer or so). The RSUs placed on opposite sides of the roadway constitute a logical unit and are connected by optical fiber under the median. Unlike inductive loop detectors, adjacent RSUs along the roadway are not connected with each other, thus avoiding the huge cost of optical fiber. Each RSU contains a GPS device (for time synchronization), an active Radio Frequency Identification (RFID) tag for communication with passing cars, a radio transceiver for RSU to RSU communication and a laptop-class computing device. The physical components of FRIEND collect traffic flow-related data from passing vehicles. The collected data is used by FRIEND\u27s inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic flow-related information along the roadway. The second contribution of this thesis is the development of an incident classification and detection algorithm that can be used to classify different types of traffic incident Then, it can notify the necessary target of the incident. We also compare our incident detection technique with other VANET techniques. Our third contribution is a novel strategy for information dissemination on highways. First, we aim to prevent secondary accidents. Second, we notify drivers far away from the accident of an expected delay that gives them the option to continue or exit before reaching the incident location. A new mechanism tracks the source of the incident while notifying drivers away from the accident. The more time the incident stays, the further the information needs to be propagated. Furthermore, the denser the traffic, the faster it will backup. In high density highways, an incident may form a backup of vehicles faster than low density highways. In order to satisfy this point, we need to propagate information as a function of density and time

Advisory Safety System for Autonomous Vehicles under Sun-glare

Autonomous Vehicles (AVs) are expected to provide a large number of benefits such as improving comfort, vehicle safety and traffic flow. AVs use various sensors and control systems to empower driver’s decision-making under uncertainties as well as, assist the driving task under adverse conditions such as vision impairment. Excessive sunlight has been recognized as the primary source of the reduction in vision performance during daytime. Sun glare oftentimes leads to an impaired visibility for drivers and has been studied from different aspects on roadways. However, there is a lack of knowledge regarding the potential detrimental effects of natural light brightness differential, particularly sun glare on driving behavior and its possible risks. This dissertation addresses this issue by developing an integrated vehicle safety methodology as an advisory system for safe driving under sun glare. The main contribution of this research is to establish a real-time detection of the vision impairment area on roadways. This study also proposes a Collision Avoidance System Under Sun-glare (CASUS) in which upcoming possible vision impairment is detected, a warning message is sent, and the speed of vehicle is adjusted accordingly. In this context, real-world data is used to calibrate a psychophysical car-following model within VISSIM, a traffic microscopic simulation tool. Traffic safety impacts are explored through the number of conflicts extracted from the microsimulation tool and assessed by the time-to-collision indicator. Conventional/human-driven vehicles and different type of AVs are modeled for a straight segment of the TransCanada highway under various AVs penetration rates. The findings revealed a significant reduction in potential collisions due to adjustment of travel speed of AVs under the sun glare. The results also indicated that applying CASUS to the AVs with a failing sensory system improves traffic safety by providing optimal-safe speeds. Furthermore, the CASUS algorithm has the potential to be integrated into driving simulators or real vehicles to further evaluate and examine its benefits under different vision impairment scenarios

Modeling and Simulation of Vehicle to Vehicle and Infrastructure Communication in Realistic Large Scale Urban Area

During the last decades, Intelligent Transportation System (ITS) has progressed at a rapid rate, which aim to improve transportation activities in terms of safety and efficiency. Car to Car or Vehicle-to-Vehicle (V2V) communications and Car/Vehicle-to-Infrastructure (I2V or V2I) communications are important components of the ITS architecture. Communication between cars is often referred to Vehicular Ad-Hoc Networks (VANET) and it has many advantages such as: reducing cars accidents, minimizing the traffic jam, reducing fuel consumption and emissions and etc. VANET architectures have been standardized in the IEEE-802.11p specification. For a closer look on V2V and V2I studies, the necessity of simulations is obvious. Network simulators can simulate the ad-hoc network but they cannot simulate the huge traffic of cities. In order to solve this problem, this thesis studies the Veins framework which is used to run a traffic (SUMO) and a network (OMNET++) simulator in parallel and simulates the realistic traffics of the city of Cologne, Germany, as an ad-hoc network. Several different simulations and performance analyses have been done to investigate the ability of different VANET applications. In the simulations, cars move in the real map of the city of Cologne and communicate with each other and also with RoadSideUnits with using IEEE 802.11p standard. Then, Probability of Beacons Delivery (PBD) in different area of a real city are calculated and also are compared with the analytical model. This study is the first research performed on calculating PBD of IEEE 802.11p in realistic large urban area. Then, the thesis focuses on modelling and analysis of the applications of the V2I in real city. In these sections, two different simulations of application of the VANET are done by developing the Veins framework and also by developing two new programs written in Python which are connected to SUMO and control the real traffic simulation. One program simulates a real city with intelligent traffic lights for decreasing response time of emergency vehicles by using V2I. The results show that using V2I communication based on 802.11p between emergency cars and traffic lights can decrease the response time of emergency cars up to 70%. Another program, simulates dynamic route planning in real traffic simulation which is used V2I and V2V communication. The result of this simulation show the capability of V2V and V2I to decrease the traveling time, fuel consumptions and emissions of the cars in the city