In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

Flows in Networks

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.

/pdf/travel-time-estimation-for-urban-road-networks-using-low-1o5qlb4jwd.pdf

Travel time estimation for urban road networks using low frequency probe vehicle data

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

An iPhone 6 using the Avenza software for capturing horizontal positions was employed to understand relative positional accuracy in an urban environment, during two seasons of the year, two times of day, and two perceived WiFi usage periods. On average, time of year did not seem to influence the average error observed in horizontal positions when GPS-only (no WiFi) capability was enabled, nor when WiFi was enabled. Observations of average horizontal position error only seemed to improve with time of day (afternoon) during the leaf-off season. During each season and during each time of day, horizontal position error seemed to improve in general during perceived high WiFi usage periods (when more people were present). Overall average horizontal position accuracy of the iPhone 6 (7-13 m) is consistent with the general accuracy levels observed of recreation-grade GPS receivers in potential high multi-path environments.

/pdf/smartphone-gps-accuracy-study-in-an-urban-environment-57pa3osyoo.pdf

Smartphone GPS accuracy study in an urban environment.

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human–machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

In this paper, we present a comparison of the Samsung Galaxy S™ [1], the Motorola Droid X™ [2], and the iPhone 4™ [3] using the real-time vehicle tracking application FreeSim_Mobile [4,5]. Using the built-in GPS receiver and the web capabilities of these smart phones, coupled with a V2I architecture, we are able to send a continuous flow of location data to a central server for processing by FreeSim [6–8], a real-time traffic simulator. The proportional model algorithm [9] is then used on this data to determine the time to traverse a section of roadway in order to report in real-time the current flow of traffic. In this paper we analyze the capability of two different Android-based [10] phones, the Samsung Galaxy S and the Motorola Droid X, on reporting accurate and reliable locations and compare them to a vehicle tracking device and the iPhone 4, which has been previously shown to be as accurate as a vehicle tracking device [4,5]. Drivers equipped with a Samsung Galaxy S, Motorola Droid X, iPhone 4, and vehicle tracking device manually timed how long it took to travel along a 0.98 mile/1.58 kilometer section of roadway. All four devices reported their location and the vehicle tracking device also reported the current speed. The amount of time to traverse the test section of roadway was determined using the proportional model algorithm [9] and compared to the actual amount of time it took to traverse the test section of roadway as manually timed. The results of the vehicle tracking device had an average error factor of 0.79% from the actual time to traverse the section of roadway, whereas the Samsung Galaxy S was 4.59%, the Motorola Droid X was 0.84%, and the iPhone 4 was found to have an error factor of 0.60%. The case study furthers the feasibly that mobile phones can act as an alternative means for probe vehicles as the iPhone 4 and Motorola Droid X are relatively as accurate as a vehicle tracking device.

Comparing the GPS capabilities of the Samsung Galaxy S, Motorola Droid X, and the Apple iPhone for vehicle tracking using FreeSim_Mobile

In this paper, we present and analyze an algorithm for mapping discrete GPS data gathered from vehicles to a continuous flow of data to determine the time to traverse a road section. Vehicle-tracking devices are installed in 80 probe vehicles in the Anchorage area, and a specific roadway section was chosen as a test section. Drivers for this study drove from before the start of the test roadway section past the end of the test roadway section, measuring the time to travel from the start to the finish of the test roadway section. The vehicle-tracking devices report speed and location every 10 seconds. From this data, we calculated the amount of time to traverse the test roadway section using our proportional model and compared it to the actual amount of time it took to traverse the test roadway section. We performed the analysis assuming the vehicle-tracking devices were reporting location every 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, and 60 seconds. With an average actual time to traverse the test roadway section of 2 minutes 28 seconds, the error rate based on the proportional model was between 1.8%–9.2% (2.7–13.1 seconds), based on how frequently the vehicle was reporting its location. Merely taking the average speed on the edge from the vehicle reporting its speed and location during those same durations had an error rate between 14.2%–25.8% (24.7–41.1 seconds). Our results show that the proportional model has a small error rate (1.8% with 10 second reporting time) and can accurately represent the time to traverse roadway sections based on discrete readings from a small number of probe vehicles.

Determining time to traverse road sections based on mapping discrete GPS vehicle data to continuous flows

In this paper, we present a comparison between the Apple iPhone 3G™ [2] and the iPhone 4™ [2] using the real-time vehicle tracking application FreeSim_Mobile [24]. The built-in GPS receiver and web capabilities of the iPhone™, coupled with a V2I architecture, are used to send a continuous flow of data to a central server for processing by FreeSim [13–15], which is a real-time traffic simulator. The proportional model algorithm [18] is then used to determine the time to traverse a roadway in order to report in real-time the current flow of traffic. At the University of Alaska Anchorage, we currently have vehicle tracking devices installed in 80 probe vehicles that traverse the Anchorage area. Due to the high cost associated with vehicle tracking devices, it is difficult to penetrate a large vehicular network on a finite amount of money, so we must look towards other available technologies, such as the constantly-expanding cellular network. In this paper we look at the iPhone 4™ capability of reporting accurate and reliable locations and compare it to the recent study of the iPhone 3G™ [24]. Drivers equipped with an iPhone 4™ cellular phone and a vehicle tracking device manually timed how long it took to travel along a 0.99 mile/1.59 kilometer section of roadway. The vehicle tracking device and the iPhone 4™ report speed and location every 10 seconds whereas the iPhone 3G™ reported every 8 seconds [24]. From this data, we calculated the amount of time to traverse the test section of roadway using the proportional model algorithm [18] and compared it to the actual amount of time it took to traverse the test section of roadway as manually timed. We found that the vehicle tracking device had an average error factor of 4.94% from the actual time to traverse the section of roadway (as determined by the stopwatch), whereas the iPhone 4™ was found to have an error factor of 1.10%. The outcome of the case study is used to determine that the iPhone 4™ has higher accuracy than a vehicle tracking device, though it is important to note that the iPhone™ is more limited than a device attached to a vehicle since it can only report its location. If paired with another third party OBD device, however, it can also send the same information as a vehicle tracking device.

Comparing the GPS capabilities of the iPhone 4 and iPhone 3G for vehicle tracking using FreeSim_Mobile

In this paper, we present a preliminary application for the iPhone™ [2] that uses the built-in GPS receiver along with the web capabilities utilizing a V2I architecture to send a continuous flow of data to a central server where FreeSim [13–15], a real-time traffic simulator, applies the proportional model algorithm [18] to determine the time to traverse a roadway in order to report in real-time the current flow of traffic. At the University of Alaska, Anchorage, we currently have vehicle tracking devices installed in 80 probe vehicles that traverse the Anchorage area. The high cost associated with vehicle tracking devices makes it difficult to penetrate a large vehicular network on limited funds, so we must look towards other available technologies, such as the constantly-expanding cellular network. In this paper we look at the iPhone™ 3G capability of reporting accurate and reliable locations by describing our sample application and comparing its reported GPS accuracy to the existing vehicle probes we have. We will then present a study of its performance of calculating an accurate traffic flow where a chosen section of roadway was driven. Drivers equipped with an iPhone™ 3G cellular phone and a vehicle tracking device manually timed how long it took to travel along the test road section. The vehicle tracking devices report speed and location every 10 seconds whereas the iPhone™ is capable of reporting the location every second, though we were receiving it every eight seconds. From this data, we calculated the amount of time to traverse the test roadway section using the proportional model algorithm and compared it to the actual amount of time it took to traverse the test roadway section. We found that the vehicle tracking device had an average error factor of 4.43% from the actual time to traverse the roadway section (as determined by the stopwatch), whereas the iPhone™ was found to have an error factor of 4.18%. The outcome of the case study is used to determine that the iPhone™ is relatively as accurate as a vehicle tracking device, though it is important to note that the iPhone™ is more limited than a device attached to a vehicle in the data it can obtain to only reporting its location.

FreeSim_Mobile: A novel approach to real-time traffic gathering using the apple iPhone™

In this paper we present the Intelligent Transportation Systems Traveling Salesman Problem (ITS-TSP), which is a heuristic algorithm loosely based on the traditional TSP with three variations: the edge weights can change constantly, not every node in the graph must be visited, and simple cycles can exist. This problem has direct application to the transportation sector where vehicles leave from a source and need to visit a certain set of locations before returning back to the source. The ITS-TSP algorithm is analyzed to show a worst case running time of O(V3), assuming that all V nodes in a graph must be visited. There is a pre-processing cost of O(V2E!) that must be incurred, though this must only be performed one time for a graph. The algorithm is a heuristic that provides routes that are optimal based on a snapshot of the graph, though as the edge weights change over time, the solution may not be optimal. On a graph of the transportation network in Anchorage, Alaska, we tested the ITS-TSP with live data gathered through vehicle-tracking devices installed in 65 vehicles. With the weight on each edge representing the amount of time to traverse a roadway, the ITS-TSP algorithm always computed the route with minimum cost based on the snapshot of the network.

Timothy Menard

Papers

Comparing the GPS capabilities of the Samsung Galaxy S, Motorola Droid X, and the Apple iPhone for vehicle tracking using FreeSim_Mobile

Determining time to traverse road sections based on mapping discrete GPS vehicle data to continuous flows

Comparing the GPS capabilities of the iPhone 4 and iPhone 3G for vehicle tracking using FreeSim_Mobile

FreeSim_Mobile: A novel approach to real-time traffic gathering using the apple iPhone™

Intelligent Transportation Systems Traveling Salesman Problem (ITS-TSP) - a specialized tsp with dynamic edge weights and intermediate cities