Evaluation of Dynamic Message Sign Operations and their Potential Impact on Traffic Pattern
Dr. Ali Haghani, CEE
The need to convey accurate travel information to motorists has become increasingly important in recent years as traffic volumes have increased and the ability to supply additional capacity no longer exists. Knowledge of rapidly changing traffic conditions gives road users the option to modify their behavior in order to avoid delays and dangerous situations. Highway Dynamic Message Signs (DMS) are often referred to as the most visible form of ITS technology. Installed in conjunction with other technologies of an Advanced Traveler Information System (ATIS), they provide the ability to enhance knowledge of the highway network for all users viewing them. In doing so, they should be able to increase overall safety and reduce congestion and delays. In Maryland, there are over 80 such DMS installed on major interstates, highways, and arterials.
An important measure of the value of a DMS message is its credibility. It is vitally important that travelers believe that a message displayed on a DMS is based on fact and accurately describes present roadway conditions. Without consistently valid information, road users will begin to ignore DMS messages altogether. In the case of travel delays, terms such as “Major Delays,” “Heavy Delays,” and “Expect Congestion” are being used to describe the prevailing conditions, however more recently estimated travel times in terms of numbers are being posted on selected signs. In order to determine the meaning and accuracy of such messages, this study will examine the road conditions under which they are displayed. Specifically, Bluetooth travel time data will be collected and analyzed during the periods of time that certain DMS messages are displayed on sample freeway corridors in Maryland.
In some corridors dynamic message signs recommend drivers to take an alternative route toward their destination. The question is do drivers comply with the message, and if the answer is positive to what extend the traffic pattern changes? While tracking every individual vehicle before and after the message display may help answering this question, however is not practical.
To address the above issues and motivated by successful application of Bluetooth detectors in freeway travel time data collection in recent years, we propose an evaluation framework that incorporates the Bluetooth generated data into historical detector data to analyze the quality, effectiveness and timeliness of messages posted on DMS, as well as changes in traffic pattern triggered by message display. This proposal involves applied and advanced research in the area of traveler information.
Automated Scalable and Real-Time Truck Parking Information System
Dr. Mehdi Kalantari, ECE
Overnight truck parking in the United States is a significant problem that is growing worse. Commercial drivers seeking to comply with the Federal Motor Carrier Safety Administration’s Hours of Service regulations often park illegally on freeway shoulders and ramps when legal parking is either not available, or the availability of free parking spots in a truck parking facility is not known . Safety is a primary issue that should be considered and one of the priority strategies in the safety area is to reduce the need for trucks to park on high speed highways. So, adequacy of rest area parking is one of the most critical issues today and has gained national importance; however, improved safety and greater operational efficiency might be realized if commercial drivers could be given real-time information on availability of parking at known locations within the general areas where they are traveling.
Posting advance parking information in real-time on upstream of each parking area, using variable road signs, making public-private partnership investments, developing and using ITS and web-based solutions, converting weigh stations near parking facilities into additional parking, allowing overnight parking at malls or large retail chains, and improving communication regarding state truck parking policies are recommendations to address the problem.
Motivated by the use of technology to improve truck parking safety through efficient use of existing parking capacity, we propose and automated real-time parking information system. This proposal involves applied and advanced research in the area of transportation and telecommunication. The proposed solution takes advantage of a low power wireless vehicle detection technology. By using this new technology the proposed parking monitoring system will be low cost, scalable, energy self sufficient and easy to deploy. Unlike imagery based technologies the system is completely anonymous and thus the privacy of truckers is not compromised.
Agent-Based Microsimulation of Long-Distance Passenger Travel Demand
Dr. Lei Zhang, CEE
The needs for analyzing transportation capital expenditure decisions at the national level in the 1970s led to two U.S. National Transportation Studies (NTS) in 1972 and 1974 respectively. These early national travel studies inventoried existing and planned U.S. transportation systems; and estimated future travel demand, system costs, performance, and broader impacts under alternative funding scenarios. With the completion of major investments on the Interstate Highway System, the development of national-level long-distance passenger travel analysis tools in the U.S. has been stagnate since the 1970s, though there have been continual academic interests in improving the theory and methods for multimodal intercity passenger travel demand analysis with a focus on mode choice.
The lack of a capable long-distance passenger travel analysis tool in the U.S. is in sharp contrast with important emerging needs for analyzing various national transportation policies related to long-distance passenger travel. For instance, it is desirable to systematically design and evaluate national transportation investment strategies, such as reconstructing and expanding the capacity of the Interstate Highway System, providing high-speed rail services along selected corridors, and building the next-generation air transportation system. In addition to these multimodal capacity investment needs for long-distance passenger travel, there are also imperative needs to assess a variety of operational and management strategies at the national level, which could significantly improve transportation efficiency and productivity, support and stimulate economic growth, and produce positive social and environmental impacts. Examples include: (1) congestion pricing on the Interstate and National Highway System; (2) Congestion management at airports; (3) Separation of passenger vehicles and heavy trucks on highway facilities; (4) National transportation financing options such as fuel tax increase and mileage fees; and (5) Substitution between long-distance travel and teleconference/telecommuting.
The objective of this proposed research project is to develop a prototype agent-based microsimulation model of long-distance passenger travel at the U.S. national level.
Mobile Sensor Network for Measuring Activity-Travel Behavior, Transportation System Performance, and Impacts of Social Networking: An Exploratory Analysis
Dr. Gang-Len Chang and Dr, Lei Zhang, CEE
So far, research based on mobile GPS platform is still preliminary and its potentials in measuring activity-travel behavior and transportation system performance, and supporting various modeling endeavors have not been adequately explored. For example, two questions that are crucial for travel demand modeling have never been well addressed in previous research. One question is with whom people are traveling. Another equal, if not more, important question about travel behavior in the context of social network is for whom people are traveling. There questions have attracted emerging and substantial interest in travel behavior research community as a growing number of studies start to focus on joint participation in activities and impacts of social network, which has been ignored for a long time partly due to lack of data. A special issue in the Journal of Transpiration has been dedicated to intra-household interactions and group decision-making. A few pilot studies extend this strand of research to investigate individual travel behavior condition upon their social network, including social activity-travel generation, spatial distribution, and information communication. A well developed data collection platform that allows researchers to monitor activity-travel behavior, transportation system performance, and impacts of social networking at the same time would provide a valuable data source and significant benefit future modeling efforts in both transportation planning and operations. The objective of this project is to develop and test a mobile sensor data collection platform that allows researchers to measure detailed activity-travel behavior, transportation system performance, and aspects of the social network related to travel at the same time based on mobile sensors with built-in GPS and/or other spatially-traceable components.
Adaptive Network Design for the Management of Large Crowds
Dr. Elise Miller-Hooks, CEE
The proposed research effort will develop behavior-based optimization techniques and solution methodologies to support an Adaptive Crowd Control System (ACCS) envisioned herein. Effective management of pedestrian movement during large public gatherings can provide crucial support toward meeting pedestrian access and safety goals. The ACCS will guide the crowd manager and security personnel toward actions that remain consistent and appropriate with a changing environment and will instruct individuals in the crowd toward efficiently and safely obtaining their goals. Poor execution of crowd management can frustrate the people in a crowd by thwarting their goals. At the extreme, poor crowd management has caused many instances of crowd crushes and fatalities involving high volumes of people in a wide array of circumstances.
A cognitive systems approach will be taken in the design of the ACCS and its components. Conventional approaches for modeling crowds are based on a physical systems modeling approach, where pedestrians are viewed as physical objects that simply react to the environment in accordance with causal physical rules specified in the models. In a cognitive systems approach, both the individuals in the crowd and the crowd manager are users of the system. These systems are assembled for the purpose of supporting the pursuit of goals at both system and individual levels. From an individual user's perspective, the goal may be to reach a particular destination by a given time, while traveling with companions. From the system's perspective, the goal may be to maximize flow rates into a particular area while maintaining lanes for emergency vehicle access and allowing users to leave an event before its conclusion.
Developed techniques will seek to provide instructions for adapting the physical and social infrastructure so as to facilitate the movement of the crowd to achieve both user and system goals. This approach recognizes that user and system goals may change in response to changes in the physical infrastructure, as well as other experiences. Individual and crowd behavior will be modeled through the use of a utility-based technique that captures heterogeneity in individual preferences and collective behavior in large public gatherings. This technique will be embedded within an optimization framework in which users selfishly seek to maximize their own utilities. No previous work has attempted to optimize the physical environment so as to support the changing desires of the system users and operators.
Dynamic Discrete Choice Models with application to Car Ownership Modeling
Dr. Cinizia Cirillo, CEE
Discrete choice models have received widespread acceptance in transport research over the past three decades, being used in travel demand modeling and behavioral analysis; however, their applications have been mainly developed in a static context. The static framework is limited by the assumption that consumers are not affected by past and future states when choosing their preferred alternative in the present. The gap between discrete choice model and dynamics in individual behavior has spurred various developments that are mainly intended to enrich the basic theory by including in the formulation the changes occurring in the system to be modeled.
A significant portion of the literature focusing on the extension of discrete choice models into a dynamic frame can be found in economics and related fields. In dynamic discrete choice structural models, agents are forward looking and maximize expected inter-temporal payoffs; the consumers is aware of the rapidly evolving nature of product attributes within a given period of time and different products are supposed to be available on the market. Changing prices and improving technologies have been the most visible phenomena in a large number of important new durable goods markets. Although sometimes the future effects are not fully known, or depend on factors that have not yet transpired, the person knows that in the future, he will maximize utility among the alternatives that will available at that time. This knowledge enables consumers to choose the alternative in the current period that maximizes his expected utility over the current and future periods.
In this project we propose a dynamic modeling framework for discrete choice and its application to the car ownership problem. It is expected that the obtained predictions will provide a more realistic picture of car user preferences under the rapid evolution of the industry supply and of the fuel prices.
Examination of the Pricing Structure of Toll Facilities to Maximize the Social Benefits
Dr. Hiroyuki Iseki, NCSG
The proposed research seeks the pricing structure—toll schedules—to not only properly allocate responsible costs of using a facility to different classes of vehicles, but also send price signals to select type of vehicles as well as choose traveling time of day and route, so that it will maximize the efficiency and benefits in the use of toll facilities—by lowering road maintenance costs, congestion costs, and vehicle maintenance costs. The proposed research will incorporate into analysis: 1) costs of maintenance of toll facilities, particularly pavement, 2) administration and operating costs of tolling, 3) operating costs of driving vehicles of different classes, 4) costs in travel time due to congestion delay, 5) maintenance costs that are incurred to vehicles by driving on uneven surface of roads, 6) toll discounts associated with the use of EZ-pass, and 7) price elasticity of demand of different vehicle classes. It should be noted that effective tolls after discounts are the prices that affect the demand of different classes of vehicles, as well as toll revenues. While toll discounts are beneficial in terms of promoting the electric tolling system that enables vehicles to drive through toll facilities without stopping and therefore reduces travel time, disproportionate discounts could induce sub-optimal demand levels of various vehicles classes.
The proposed research goes beyond the studies based on the highway cost allocation model (HCAM). While the results from such studies are quite useful to get a sense of the distribution of costs (capital costs and maintenance costs) of facilities among different vehicle classes by different number of axels, the static nature of the model does not adequately answer how revised toll schedules would affect the demand of each type of vehicle, and how the different mixture of vehicles would result in different level of road damage as well as traffic congestion on facilities. In addition, the studies based on the HCAM take into account the costs on the supply side, but not the costs on the demand side, and does not necessarily maximize the net benefits for the society.
While the initial research will address the pricing structure for the existing toll facilities in Maryland—6 bridges and 1 tunnel, the proposed research could be extended to take into account initial capital investment decision for the highway network in the state. Taking into account the State Highway Administration’s consideration for express toll lanes (ETLs) on several corridors, such as I-95, I-270, I-495/I-95, and MD 5, in order to address traffic congestion and negative environmental impacts, provide alternative modes of travel, and develop an integrated highway system that optimizes efficiency and maximizes flexibility, the proposed research could be expanded also to include these corridors. Furthermore, the same approach could be applied to toll facilities and highway networks in other states. The analysis results are expected to show optimal toll schedules, cost savings in facility maintenance, travel time, vehicle maintenance, demand levels of different class vehicles, toll revenues, and an increase in social welfare (changes in costs).
Intelligent, Multi-Camera Transportation Infrastructure Surveillance and Monitoring with High Data Rate Wireless Information Transfer and Networking
Dr. Chris Davis, ECE and Dr. Stuart Milner, CEE
In this new project we will leverage our previous successes in CITSM and develop new capabilities for traffic surveillance and monitoring systems that use multiple cameras to simultaneously detect and track multi object motion as well as conduct anomaly detection and classification in real-time. The project will build on our already developed single camera-based object detection systems by developing and integrating a second, cooperating camera, which can autonomously zoom, pan and tilt in the process of tracking traffic incident. Having second camera “focus” on the persistent tracking of, and zooming in, on incidents allows the first camera to simultaneously detect and track the larger field of view or traffic scene.
In other words, it is like having two sets of “eyes” monitoring the traffic—one set of eyes monitoring the ongoing events (e.g., multiple lane highways, intersections, freeway ramps, parking lots, etc.) and the other set of eyes following the critical event such as an accident or sudden change in traffic patterns). The introduction of multiple coupled cameras and new algorithms for surveillance make this project very distinct from our other related work. Our research is unique in that we combine expertise in calibrated imaging hardware with advanced wireless networking and intelligent image analysis software that works with the hardware in real-time. Our technology can provide situation alerts to operations centers in real time. In contrast to all currently deployed conventional and analog-based video monitoring systems, we use uncompressed, high-definition (HD) camera images that allow real time analysis of multiple “events” (e.g., vehicle speed, type, number, pedestrians, etc.) and anomalies that would be missed by conventional compressed CCTV. In addition, following detected events that meet pre-defined criteria, we provide persistent follow-up and tracking using one camera and autonomous, calibrated zoom, pan and tilt using a second camera.
Flexibility and Responsiveness in Public Transportation Systems
Dr. Paul Schonfeld, CEE
The flexibility of a system may be defined in terms of its ability to adjust to different conditions. For a public passenger transportation system those conditions might include widely different demand patterns and densities at various locations and times, different user categories (e.g. elderly & handicapped, children, groups, unfamiliar users), traffic congestion and blockages, geometrically constricted roads and severe weather. Responsiveness connotes the speed and ease with which adjustments to circumstances can be made and user requests for service can be accommodated. Reliability indicates the extent to which expected service levels can be met despite disruptions (e.g. weather, traffic, sports events,) and possible failures in some system components. A system’s reliability is also judged by the predictability and steadiness of its performance, which greatly concerns many passengers.
The willingness and ability of travelers to use public transportation services depends greatly on the accessibility, reliability, and convenience of those services. Conventional public transit services (which include most bus and rail transit services) can provide relatively high passenger-carrying capacities at relatively low average costs to system operators, but their service quality is limited since passengers must somehow reach some predetermined stations, wait for a vehicle, possibly transfer several times, and then move from their exit stations to their destinations. Thus, conventional transit services are most disadvantaged in areas and time periods with low demand densities, which cannot economically sustain high route densities and service frequencies.
Some paratransit services can provide more flexible routes and schedules, possibly with door-to-door service, and special assistance for handicapped passengers. Thus, taxis provide very high service flexibility, but at high unit costs (especially in labor cost per passenger-mile). Various forms of ridesharing and subscription services considered in this proposed study can provide intermediate combinations of unit cost and service quality.
To some extent, efficiency in public transportation services might be sought by identifying the most appropriate modes and service types for different areas, periods and user groups. However, the problem is considerably more complex since the areas, periods and user groups within a metropolitan region are usually quite interrelated. Excessive specialization usually also results in underutilization of resources (e.g. vehicles and drivers). To achieve desirable levels of flexibility and efficiency we must also seek to integrate systems so that resources can be switched among different routes and service types, as circumstances change.
In the applied research study proposed here we will develop performance measures that quantify the flexibility and responsiveness of public transportation systems and then develop mathematical models for optimizing flexible and responsive public transportation systems.
Bridge Health Monitoring System Based on Flexible, Wireless, and Batteryless Patch Sensors
(Technical Assistance Funding)
Dr. Mehdi Kalantari , ECE
According to the National Bridge inventory Database of the Federal Highway Administration in year 2008, the U.S. transportation infrastructure has 601,027 bridges from which 71,429 are rated as structurally deficient. State of Maryland is not an exception of this issue. According to the above database, the State has a total of 5,168 bridges from which 396 are rated as structurally deficient. Structural health monitoring is required to anticipate the impending failure of bridges – as well as other critical infrastructure such as pipelines, railways, and drilling platforms. Yet, existing instrumentation techniques for structural health monitoring of bridges suffer from non-scalability due to high cost of instrumentation devices, large installation costs (e.g., due to wiring needs), or high maintenance costs. Currently, the only practice for monitoring the health of bridges is a mandated bi-annual manual inspection; however, manual inspection has proved extremely insufficient to ensure safety of bridges, as such inspections do not provide enough information to prevent catastrophic failures. To protect the infrastructure systems against aging, structural malfunction, and collapse, this project will offer a cost effective and scalable solution for the real time monitoring of important structural state quantities such as stress, strain, fatigue cracks, vibration, etc. The solution is based on patent pending Active RF Test (ART) technology, which incorporates novel sensing, energy harvesting, and wireless communication technologies into a flexible, wireless, and batteryless sensor. The proposed approach is capable of accurately identifying structural distress such as overstrain, crack initiation and growth, and deformation. Key advantages of the proposed system include (1) flexibility and compact size of sensors, allowing them to be applied to curved surfaces and complex geometries; (2) wireless operation with a self-contained energy harvesting device as a power supply; and (3) very low cost, enabling a large scale distribution in which data fusion techniques can be utilized for enhanced damage severity evaluation and source location.
Sustainability Impact of Multimodal Corridor Improvements in Urbanized Area
Dr. Lei Zhang, CEE
This research project will support the existing SHA Sustainability Initiative and CHC Program with the following objectives:
- Develop methods to analyze the planning-level sustainability impact (i.e. mobility, safety, energy and environment, natural resources, socio-economic, and cost) of multimodal improvements on
highway corridors, including:
- Road diet (i.e. lane removal)
- High occupancy vehicle (HOV) lane
- High occupancy toll (HOT) lane
- Bus rapid transit/bus-only lane
- Light rail transit
- Truck-only lane
- Express toll lane
- Park-and-ride facility
With these enhancements, MOSAIC will be capable of analyzing the long-term sustainability impact of both highway and multimodal corridor improvements in urban and rural areas in Maryland.
Developing a Data and Modeling Framework for Integrated Transportation Operations and Planning
Dr. Lei Zhang, CEE
This research project has three main objectives:
- Develop a route choice/traffic diversion model based on existing travel behavior data for the ICC (Inter- County Connector) study area.
- Develop a peak spreading model to capture departure time choice decisions based on existing travel behavior data for the ICC study area.
- Integrate the traffic diversion and peak spreading models with a microscopic traffic simulation model for the ICC study area