Showing papers on "Intelligent transportation system published in 2007"

Current map-matching algorithms for transport applications: State-of-the art and future research directions

Abstract: Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.

Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems

Abstract: Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide-ranging applications, well beyond their design purpose. This paper illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer and what is needed in practice in transit agencies. To close this gap requires data processing/analysis methods with support of technologies such as database management systems and geographic information systems. This work presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin-destination (OD) matrices from an origin-only AFC system to replace expensive passenger OD surveys. A software tool is created to facilitate the method implementation. Results of the application in CTA are given.

Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

Abstract: In this paper, we demonstrate a driver intent inference system that is based on lane positional information, vehicle parameters, and driver head motion. We present robust computer vision methods for identifying and tracking freeway lanes and driver head motion. These algorithms are then applied and evaluated on real-world data that are collected in a modular intelligent vehicle test bed. Analysis of the data for lane change intent is performed using a sparse Bayesian learning methodology. Finally, the system as a whole is evaluated using a novel metric and real-world data of vehicle parameters, lane position, and driver head motion.

Queuing Network Modeling of Driver Workload and Performance

Abstract: Drivers overloaded with information significantly increase the chance of vehicle collisions. Driver workload, which is a multidimensional variable, is measured by both performance-based and subjective measurements and affected by driver age differences. Few existing computational models are able to cover these major properties of driver workload or simulate subjective mental workload and human performance at the same time. We describe a new computational approach in modeling driver performance and workload-a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. This modeling approach not only successfully models the mental workload measured by the six National Aeronautic and Space Administration Task Load Index workload scales in terms of subnetwork utilization but also simulates the driving performance, reflecting mental workload from both subjective- and performance-based measurements. In addition, it models age differences in workload and performance and allows us to visualize driver mental workload in real time. Further usage and implementation of the model in designing intelligent and adaptive in-vehicle systems are discussed.

Local Density Estimation and Dynamic Transmission-Range Assignment in Vehicular Ad Hoc Networks

Abstract: Vehicular ad hoc networks have several characteristics that distinguish them from other ad hoc networks. Among those is the rapid change in topology due to traffic jams, which also disturbs the homogeneous distribution of vehicles on the road. For this reason, a dynamic transmission range is more effective in maintaining the connectivity while minimizing the adverse effects of a high transmission power. This paper proposes a scheme that allows vehicles to estimate the local density and distinguish between the free-flow and the congested traffic phases. The density estimate is used to develop a dynamic transmission-range-assignment (DTRA) algorithm that sets a vehicle transmission range dynamically according to the local traffic conditions. Simulations of several road configurations validate the quality of the local density estimation and show that the DTRA algorithm is successful in maintaining the connectivity in highly dynamic networks.

Adaptive Traffic Light Control with Wireless Sensor Networks

Abstract: In this paper, we propose a novel decentralized traffic light control using wireless sensor network. The system architecture is classified into three layers; the wireless sensor network, the localized traffic flow model policy, and the higher level coordination of the traffic lights agents. The wireless sensors are deployed on the lanes going in and out the intersection. These sensors detect vehicles' number, speed, etc. and send their data to the nearest Intersection Control Agent (ICA) which, determines the flow model of the inter- section depending on sensors' data (e.g., number of vehicles approaching a specific intersection). Coping with dynamic changes in the traffic volume is one of the biggest challenges in intelligent transportation system (ITS). Our main contribution is the real-time adaptive control of the traffic lights. Our aim is to maximize the flow of vehicles and reduce the waiting time while maintaining fairness among the other traffic lights. Each traffic light controlled intersection has an intersection control agent that collects information from the sensor nodes. An intersection control agent manages its intersection by controlling its traffic lights. Multiple intersection agents can exchange information among themselves to control a wider area.

Self-Configuring TDMA Protocols for Enhancing Vehicle Safety With DSRC Based Vehicle-to-Vehicle Communications

Abstract: This paper presents a novel Medium Access Control protocol for inter-vehicular wireless networking using the emerging Dedicated Short Range Communication (DSRC) standards. The main contribution of the paper is the design of a self- configuring TDMA protocol capable of inter-vehicle message delivery with short and deterministic delay bounds. The proposed Vehicular Self-Organizing MAC (VeSOMAC) is designed to be vehicle location and movement aware so that the MAC slots in a vehicle platoon can be time ordered based on the vehicles' relative locations for minimizing the multi-hop delivery delay. A novel feature of VeSOMAC is its in-band control mechanism for exchanging TDMA slot information during distributed MAC scheduling. It is shown that by avoiding explicit timing information exchange, VeSOMAC can work without inter-vehicle time synchronization. The in-band control mechanism is also used for fast protocol convergence during initial network setup and topology changes due to vehicle movements. A simulation model has been developed for comparing VeSOMAC's performance with that of DSRC-recommended 802.11 MAC protocol for highway traffic safety applications.

Intelligent transport systems and effects on road traffic accidents: State of the art

Abstract: The results from several reviews have been presented and the aspects of road safety associated with intelligent transport systems (ITS) applications have been addressed. The attempt is to make a state-of-the-art regarding effects on accidents by categorising systems according to levels of evaluations methods that have been applied. These categories are effects on behaviour, effects on accidents by proxy/surrogate methods, accident studies from real traffic, effects on accident types and finally by meta-analysis where weighted estimates of effects on accidents can be calculated. Thirty-three IT systems including driver assistance systems/advanced driver assistance systems, in-vehicle information systems, in-vehicle data-collection systems and road telematics have been listed. Effects based on meta-analysis are estimated for 11 systems, and single accident studies are found for an additional 2 systems. For the remaining 20 systems, no studies from real road traffic have been identified. Effects on accidents of antilocking brake systems and electronic stability control (ESC) are presented in more detail according to their effects on certain accident types. ESC appears to be very efficient in reducing the number of accidents. Behavioural adaptations to ITS are considered and discussed, especially in terms of compensation mechanisms. Four hypotheses regarding prediction of effects on accidents are stated according to whether systems increase or decrease 'windows of opportunities' by calling upon a driver behaviour model where emotions play a central role

REDUCING GREENHOUSE EMISSIONS AND FUEL CONSUMPTION : Sustainable Approaches for Surface Transportation

Abstract: Climate change is rapidly becoming known as a tangible issue that must be addressed to avoid major environmental consequences in the future. Recent change in public opinion has been caused by the physical signs of climate change–melting glaciers, rising sea levels, more severe storm and drought events, and hotter average global temperatures annually. Transportation is a major contributor of carbon dioxide (CO2) and other greenhouse gas emissions from human activity, accounting for approximately 14 percent of total anthropogenic emissions globally and about 27 percent in the U.S.u Fortunately, transportation technologies and strategies are emerging that can help to meet the climate challenge. These include automotive and fuel technologies, intelligent transportation systems (ITS), and mobility management strategies that can reduce the demand for private vehicles. While the climate change benefits of innovative engine and vehicle technologies are relatively well understood, there are fewer studies available on the energy and emission impacts of ITS and mobility management strategies. In the future, ITS and mobility management will likely play a greater role in reducing fuel consumption. Studies are often based on simulation models, scenario analysis, and limited deployment experience. Thus, more research is needed to quantify potential impacts. Of the nine ITS technologies examined, traffic signal control, electronic toll collection, bus rapid transit, and traveler information have been deployed more widely and demonstrated positive impacts (but often on a limited basis). Mobility management approaches that have established the greatest CO2 reduction potential, to date, include road pricing policies (congestion and cordon) and carsharing (shortterm auto access). Other approaches have also indicated CO2 reduction potential including: low-speed modes, integrated regional smart cards, park-and-ride facilities, parking cash out, smart growth, telecommuting, and carpooling. (A)

Advanced Motion Control and Sensing for Intelligent Vehicles

Abstract: Advanced Tire Friction Modeling and Monitoring.- Advanced Vehicle Lateral Motion Control.- Advanced Vehicle Longitudinal Motion Control.- Advanced Vehicle Vertical Motion Control.- Advanced Individual Vehicle Motion Control.- Advanced Multiple Vehicles Motion Control.- Intelligent Vehicle Vision Systems.- Intelligent Vehicle Tire Inspection and Monitoring.

Offline Calibration of Dynamic Traffic Assignment: Simultaneous Demand-and-Supply Estimation

Abstract: Advances in intelligent transportation systems have resulted in deployment of surveillance systems that automatically collect and store extensive networkwide traffic data. Dynamic traffic assignment (DTA) models have been developed for a variety of dynamic traffic management applications. They are designed to estimate and predict the evolution of congestion with detailed models and algorithms that capture travel demand and network supply and their complex interactions. The availability of rich time-varying traffic data spanning multiple days provides the opportunity to calibrate a DTA model’s inputs and parameters offline so that its outputs reflect field conditions in future offline and online real-time applications. The state of the art of DTA model calibration is a sequential approach, with supply model calibration (assuming known demand inputs) followed by demand calibration with fixed supply parameters. An offline DTA model calibration methodology is presented for simultaneous estimation of all demand-and-supply inputs and parameters, with sensor data. A minimization formulation that can use any general traffic data and present scalable solution approaches for the complex, nonlinear, stochastic optimization problem is adopted. A case study with DynaMIT, a DTA model with traffic estimation and prediction capabilities, is used to demonstrate and validate the methodology. Archived sensor data and a network from Los Angeles, California, are used to demonstrate scalability. Results indicate that the simultaneous approach significantly outperforms the sequential state of the art in terms of modeling accuracy and computational efficiency.

Lane-Change Detection Using a Computational Driver Model

Abstract: OBJECTIVE: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. METHOD: Using a "model tracing" methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. RESULTS: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. CONCLUSION: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. APPLICATION: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems. Language: en

Towards high accuracy road maps generation from massive GPS Traces data

Abstract: We present a novel approach to dynamically generate high accuracy road maps through the statistical analysis of in- vehicle GPS trace data. Our approach opens a possibility to implement an optional system in a very economic way for mapping roads to support the applications in Intelligent Transportation Systems (ITS). The results presented in this paper show the potential with respect to the map update technology.

An Infrastructure-Free Traffic Information System for Vehicular Networks

Abstract: Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic information processing. This has widespread applications in the context of vehicular networks. The existing centralized approaches for traffic estimation are characterized by longer response times. They are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and infrastructure-free mechanism for road density estimation. The proposed solution is adaptive and scalable and targets city traffic environments. The approach is based on the distributed exchange and maintenance of traffic information between vehicles traversing the routes. The performance analysis of the proposed mechanism shows the accuracy of the algorithm for different traffic densities. It also gives insights into the promptness of information delivery in the mechanism based on delay analysis at road intersections. This promptness is a necessary condition to various applications requiring reliable decision making based on road traffic awareness.

Dynamic Taxi-Sharing Service Using Intelligent Transportation System Technologies

Abstract: A practical and applicable taxi-sharing system based on the use of intelligent transportation system (ITS) technologies has been developed in Taipei City. This system is easy for members to use and inexpensive for the service provider to operate. This paper gives an overview of the taxi-sharing service, presents key algorithms for dynamic rideshare matching processes, describes the field trial operation of the system in Taipei Nei-Hu Science and Technology Park and discusses empirical results to provide valuable implications for better taxi-sharing service in the future.

Markov-Based Lane Positioning Using Intervehicle Communication

Abstract: The majority of today's navigation techniques for intelligent transportation systems use global positioning systems (GPS) that can provide position information with bounded errors. However, due to the low accuracy that is experienced with standard GPS, it is difficult to determine a vehicle's position at lane level. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within communication range, the lane positions of vehicles can be found. The algorithm's effectiveness is shown in both simulations and experiments with real data.

Optimal power management of plug-in HEV with intelligent transportation system

Abstract: Hybrid electric vehicles (HEV) have demonstrated their capability of improving the fuel economy and emission. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of HEV. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. In the past, the trip information has been considered as future information for vehicle operation and thus unavailable a priori. This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board geographical information systems (GIS), global positioning systems (GPS) and advanced traffic flow modeling techniques. In this paper, a new approach of optimal power management of PHEV in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the final time of the cycle. The vehicle model was based on a hybrid SUV. Only fuel consumption is considered for the current stage of study. Simulation results showed significant improvement in fuel economy compared with rule-based power management. Furthermore, simulations on several driving cycles using the proposed method showed much better consistency in fuel economy compared to the rule-based control.

Modeling the process of information relay through inter-vehicle communication

Abstract: In a new paradigm of the decentralized traffic information system as a recent thrust in the Intelligent Transportation Systems (ITS), vehicles form ad hoc mobile networks, and information may be propagated between vehicles through wireless communication with a short transmission range. Fundamental to the system design is effective information propagation. In this paper, we study information propagation along a traffic stream on which presence of equipped vehicles follows an independent homogeneous Poisson process. We define a relay process in which only the furthest equipped vehicle within each transmission range continues the relay, and model it as a transient Markov process. We present closed form formulas for the expected value and variance of propagation distance in the case without transmission delay. We also study the expected number of relays and the expected propagation distance in the case with transmission delay. The results make transparent the relationship between propagation distance, equipped vehicle density and transmission range. In addition, we study the probability distribution of propagation distance, and find that the Gamma distribution could be used as a good practical means of approximation especially when the number of equipped vehicles is large within a transmission range. The Gamma-like behavior is also observed on heterogeneous traffic. It is noted that the relay process has many other applications as well.

NCTUns 4.0: An Integrated Simulation Platform for Vehicular Traffic, Communication, and Network Researches

Abstract: In this paper, we present an integrated simulation platform, called NCTUns, for vehicular traffic, communication, and network researches. This platform combines the capabilities supported by a network simulator and those supported by a traffic simulator. With these simulation capabilities, NCTUns can be used to design protocols for intelligent transportation systems (ITS) communication networks such as a wireless vehicular communication network. Besides, the novel architecture of the platform enables the real-world Linux protocol stack and any real-world application to be used in simulations of such networks. In this paper, we present the design of NCTUns for supporting ITS researches and show its scalability.

The Selective Random Subspace Predictor for Traffic Flow Forecasting

Abstract: Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow data may be incomplete (partially missing or substantially contaminated by noises), which will aggravate the difficulties for traffic flow forecasting. In this paper, a new approach, termed the selective random subspace predictor (SRSP), is developed, which is capable of implementing traffic flow forecasting effectively whether incomplete data exist or not. It integrates the entire spatial and temporal traffic flow information in a transportation network to carry out traffic flow forecasting. To forecast the traffic flow at an object road link, the Pearson correlation coefficient is adopted to select some candidate input variables that compose the selective input space. Then, a number of subsets of the input variables in the selective input space are randomly selected to, respectively, serve as specific inputs for prediction. The multiple outputs are combined through a fusion methodology to make final decisions. Both theoretical analysis and experimental results demonstrate the effectiveness and robustness of the SRSP for traffic flow forecasting, whether for complete data or for incomplete data

Thin client intelligent transportation system and method for use therein

Abstract: A thin client intelligent transportation system wherein geospatial roadmaps and map matching systems are maintained in roadside nodes and are more fully exploited. The thin client approach offers significant advantages over thick client approaches that rely on on-vehicle maps and map matching systems, including reduced complexity of on-board equipment and elimination of map integrity issues. The thin client approach also offers significant advantages over systems wherein the vehicle is required to access maps and map matching systems in real-time from a remote data center, including the ability to meet the low latency requirements for many vehicle safety applications. The present invention in some embodiments has added advantages in that it exploits roadside maps and map matching systems in revenue generating applications that are not directly related to passenger safety.

Reliable and Effcient Information Dissemination in Intermittently Connected Vehicular Adhoc Networks

Abstract: Many applications of vehicular adhoc networks (VANET) to intelligent transportation systems require reliable, bandwidth-efficient dissemination of traffic and road information via adhoc network technology. This is a difficult task since intervehicular networks often lack continuous end-to-end connectivity and are characterised by large variations in node density. In this paper we introduce a new epidemic protocol for information dissemination in highly dynamic and intermittently connected VANET. We show through realistic simulations in highway traffic that our protocol is capable of reliable and efficient information dissemination in VANET in the face of frequent network fragmentation and large density variations. In addition to VANET, our proposed algorithm may find applications in the context of disruption tolerant networks (DTN).

Merge Algorithms for Intelligent Vehicles

Abstract: There is an increased concern towards the design and development of computercontrolled automotive applications to improve safety, reduce accidents, increase traffic flow, and enhance comfort for drivers. Automakers are trying to make vehicles more intelligent by embedding processors which can be used to implement Electronic and Control Software (ECS) for taking smart decisions on the road or assisting the driver in doing the same. These ECS applications are high-integrity, distributed and real-time in nature. Inter-Vehicle Communication and Road-Vehicle Communication (IVC/RVC) mechanisms will only add to this intelligence by enabling distributed implementation of these applications. Our work studies one such application, namely Automatic Merge Control System, which ensures safe vehicle maneuver in the region where two roads intersect. We have discussed two approaches for designing this system both aimed at minimizing the Driving-Time-To-Intersection (DTTI) of vehicles, subject to certain constraints for ensuring safety. We have (i) formulated this system as an optimization problem which can be solved using standard solvers and (ii) proposed an intuitive approach namely, Head of Lane (HoL) algorithm which incurs less computational overhead compared to optimization formulation. Simulations carried out using Matlab and C++ demonstrate that the proposed approaches ensure safe vehicle maneuvering at intersection regions. In this on-going work, we are implementing the system on robotic vehicular platforms built in our lab.

Driving into Intelligent Spaces with Pervasive Communications

Abstract: Recent advances in digital modulation and transmission, signal processing, wireless access protocols, and IC technology have spawned many intelligent devices and objects. A good example are intelligent transportation spaces (ITSP) which have shown many potential benefits including driving safety, transport efficiency, and comfort that accrue from increased traffic information, reduced driving loads, and improved route management. Although wireline communications might enable some ITSP requirements, the inherent mobility, flexibility and scalability of wireless communications clearly make them critical. Enabling technologies in this area include an array of wireless solutions such as ISM bands, DSRC bands, UWB radio, and MAC protocols

An industrial network flow information integration model for supply chain management and intelligent transportation

Abstract: Industrial network flow involves three domains: infrastructure, individual subjects of movement, and planning and control of the movement. Examples include supply chain and intelligent transportation. These traditionally isolated domains can be digitally connected to enhance their performance. Digitization of the infrastructure provides real-time data to facilitate its operation, while digitally connecting the subjects to the infrastructure allows for tailored services and support to particular subjects. Connection of both to the enterprise information systems enables adaptive control for the application (e.g. logistics) at a global optimization level. Previous results in the field cover separate aspects of planning/routing, real-time monitoring, and trip support. Toward this end, a new highway-based subject-infrastructure-enterprise (SIE) information integration model using digital connection is proposed to the field of industrial network flow control for application to intelligent transportation and supply chain management. The SIE model supports industrial network flow control in a way comparable to an adaptive control panel administering an automated material handling system. In this metaphor, the global infrastructure becomes 'controllable' similar to factory conveyors and automated guided vehicles. This paper presents a conceptual design substantiated with information requirements analysis. An empirical experiment at locations in New York State shows the technical feasibility of the digital connection envisioned.

VANET-DSRC Protocol for Reliable Broadcasting of Life Safety Messages

Abstract: This paper proposes a novel protocol for reliable broadcasting of life safety messages in Vehicular Ad-hoc Networks (VANETs) simulating reactions of car drivers. In case of any dramatic change of speed or moving direction, the vehicle is considered abnormal and hence it transmits an emergency warning message over the control channel of the Dedicated Short-Range Communication Protocol (DSRC). The proposed protocol gives the vehicle in the most dangerous situation the highest priority to transmit the acknowledgement signal. The choice of that vehicle is done locally based on the location, direction, and speed of the receiving vehicle. The superiority of the proposed protocol over existing protocols is highlighted conceptually and with simulations.

A Low-cost Vehicle Detection and Classification System based on Unmodulated Continuous-wave Radar

Abstract: Vehicle detection and classification system is an important part of the intelligent transportation systems (ITS). Its function is to measure traffic parameters such as flow-rate, speed, and vehicle types, which are valuable information for applications of road surveillance, traffic signal control, road planning, and so on. This paper presents a novel low-cost vehicle detection and classification system which is based on a K-band unmodulated CW radar. This system utilizes time-frequency analysis, multi-threshold detection, and Hough transform as the major signal processing methods to extract speed and shape information of vehicles from Doppler signature they generate. It can perform vehicle detection, speed measurement, and vehicle classification simultaneously. Experimental results show that the proposed system and algorithms can provide promising performance and accuracy.

“War Rooms” of the Street: Surveillance Practices in Transportation Control Centers

Abstract: This article investigates the surveillance dimensions of “intelligent transportation systems” in the United States, with a particular focus on the mediation of data by engineers in transportation control centers. These communication systems lend themselves to surveillance by means of “function creep” beyond their primary intended purposes and through the everyday collection and manipulation of data to manage mobilities. In the U.S., dominant system protocols privilege vehicular throughput and discipline those who deviate from that norm.