Fengxiang Qiao

Approach to Development and Evaluation of Driving Cycles for Classified Roads Based on Vehicle Emission Characteristics

The first objective of this study is to develop driving cycles for classified roads incorporating a vehicles driving activities as well as emission characteristics by using the data collected by a portable emission measurement system, which was not available or has never been used in developing driving cycles. The second objective is to develop an approach to evaluate driving cycles in which the vehicle specific power, a parameter that can readily connect the driving modes with emissions, is used to evaluate how well the driving cycles can represent the driving and emission characteristics on real roads. A comparison is conducted of driving cycles developed by the proposed method versus the traditional methodology. Results show that driving cycles developed by the proposed method can better represent the emission characteristics of on-road vehicles.

Drivers’ Smart Assistance System Based on Radio Frequency Identification: Enhanced Safety and Reduced Emissions in Work Zones

A work zone is an area of roadway with construction, maintenance, or utility work activities. According to NHTSA, many traffic accidents and fatalities happen in work zones. Many traditional safety countermeasures have been utilized in work zones, including installing special signs and barriers and posting suitable speed limits. However, these measures have not fully solved the problem. Radio frequency identification (RFID) is a powerful, globally accepted technology that has had great success in business management for decades and can be employed as an advisory device that communicates between vehicles and infrastructure in work zones. The purpose of this study was to develop an RFID-based driver smart assistance system (DSAS) to improve traffic safety and air quality in work zones. The RFID device enabled communication between the vehicle and the roadside on a real-time basis and was supplemented with GPS and other sensors for dynamic traffic management. Such an assistance system would provide suitable verbal and visual warning messages to drivers approaching a work zone. Road tests with the DSAS were conducted in Houston, Texas, with 20 drivers, and the systems impact on vehicle speeds, safety, and vehicle emissions was examined. Statistical results from the tests showed that the DSAS helped drivers take action earlier to decelerate and reduce speeds. All test subjects evaluated the system as satisfactory. This system can also help to reduce most types of vehicle emissions. Further testing and improving of this promising system is recommended in broader fields.

Genetic Algorithm–Based Approach to Develop Driving Schedules to Evaluate Greenhouse Gas Emissions from Light-Duty Vehicles

A city-specific driving schedule is developed for evaluating greenhouse gas emissions [specifically carbon dioxide (CO2)] from light-duty vehicles by using the data collected by a portable emission measurement system and a Global Positioning System device. A genetic algorithm is used to find the optimum driving schedules composed of a combination of microtrips selected from a pool of microtrips. Four types of assessment measures—Type I, driving activity; Type II, driving operating mode distribution; Type III, fuel consumption rate; and Type IV, the product of Types II and III—are adopted in developing the driving schedules. With the MOVES emission modeling approach, the developed driving schedules are used to predict CO2 emissions, which are then compared with and evaluated by the real-world CO2 emissions. Performance evaluation results show that the driving schedules developed by the driving operating mode distribution measure can result in more accurate CO2 emission estimates.

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

ABSTRACT Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers’ driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian’s crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects’ driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers’ driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (p-values range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones. Implications: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones.

Identifying Demographical Effects on Speed Patterns in Work ZonesUsing Smartphone Based Audio Warning Message System

Objective: The objective of this research is to investigate the impacts of drivers’ demographic factors on speed patterns in response to a smartphone based warning message, while driving through the advance warning area of a work zone. Methodology: A smartphone application was developed using Massachusetts Institute of Technology (MIT) App Inventor 2, which was used to provide test drivers with a warming message on traffic control and incident awareness. Twenty-four subjects with different demographic features (different gender, age, education background, and driving experience) were recruited to drive through an advance warning area of a work zone twice in two scenarios (with and without the warning message). The advance warning area was divided into three segments for the convenience of analysing the significant difference in subjects’ reactions to the warning messages and the static traffic control signs, in terms of speed patterns. Findings: Under a traditional traffic control, drivers’ driving speed patterns were not significantly sensitive to the four studied socio-demographic features; but their mean driving speeds and speed variance were noticeable higher than in the situation with an audio warning message. When the smartphone-based messages were provided, drivers drove noticeably slower within the work zone, and the variance became narrower in the most studies of sociodemographic features. Experienced drivers and highly educated drivers drove significantly slower after receiving a warning message from the second and third segment (AWM 2 and 3). Conclusion: The smartphone-based warning messages were able to help drivers to control their driving speed better for cautious driving in a work zone area, especially for the experienced and highly educated drivers driving through a merging area and an activity area of workers.

On-Road Vehicle Emission and Activity Data Collection and Evaluation in Houston, Texas

The newly developed on-road emission measurement device OEM-2100 was used to collect emissions in the Houston, Texas, area. The device can measure second-by-second fuel consumption and emissions of nitrogen oxides, hydrocarbons, carbon monoxide, carbon dioxide, and particulate matter. A total of 459.0 mi of on-road tests and 813.9 min of idling tests were conducted on three passenger cars and two trucks under 170 different test conditions (170 bags placed). Global Positioning System data were recorded simultaneously in line with the emission data. Data were analyzed by a six-step data processing procedure. The bag-based analysis indicated that vehicle emissions varied strongly, not only with vehicle activity data but also with roadway facility types and vehicle specifications. Spatial distributions of tested emissions illustrated how the emissions altered along the driving routes. The tested vehicle emissions were compared with the MOBILE6.2 estimates, and significant differences were found for all vehicles and for most testing conditions. Among the roadway facility types, the largest difference was on arterial roads, where the tested on-road emissions were higher than MOBILE6.2 estimates. As for idling conditions, the tested emissions were much higher than MOBILE6.2 estimates and indicates a need for further investigation of idling emissions. The large amount of emission and vehicle activity data collected initiated a useful database in Houston with promising potential uses. More on-road vehicle emission tests are necessary to obtain more accurate and reliable local vehicle emission individuality and to establish a richer on-road emission database.

Implications of Smartphone Messages on Driving Performance along Local Streets

The driver’s driving performance may affect the operation of transportation system in terms of safety, mobility, and air quality. Particularly, in a complicated traffic situation such as a local community area with multiple school zones and other public activity facilities, driver’s driving performance becomes critical to the safety of road users within the neighborhood. Many studies have demonstrated that driver’s driving performance could be altered by advance warning messages in the forms of either image or sound. With the advent of innovative wireless communication technologies, various smartphone applications have been developed to provide drivers with abundant information, so as to enhance traffic operations. In this paper, the implications of one such smartphone applications were evaluated through an on-road test in a complicated community area in Houston, Texas, United States. This study focused on the measurement of a following vehicle’s driving performance in a local street area. Three scenarios with two different speed limits were designed to explore the impacts of warning messages from the smartphone application and the sensitivity of message settings on driving performance (speed profile, deceleration rates, and braking distance). Results show that, the smartphone messages could improve the following vehicle’s performance by smoothing the speed profiles and deceleration rates, and extending the braking distances to either the leading vehicle or the stop line at an intersection, when the leading vehicle was approaching a stop sign controlled intersection, or slowed down for turning movement.

Clustering Pavement Roughness Based On the Impacts on VehicleEmissions and Public Health

Objective: This research intends to explore the correlation between pavement roughness and vehicle emissions, and classify the pavement roughness based on vehicle emissions and public health impacts. Method: On-road tests were conducted to measure vehicle emissions by a Portable Emission Measurement System (PEMS), and collect the corresponding pavement roughness by a smartphone application. A total of 19,038 data pairs were collected during 325 km long test routes in the State of Texas. The correlation of the emissions and International Roughness Index (IRI) are analyzed and the roughness was classified into clusters by three pattern recognition algorithms. Findings: The pavement roughness could be classified into four categories based on the clustering features of emission factors. The average of Normalized Emission Factor (ANEF) started with 0.051 at a level of IRI between 0+ and 1.99 m/km (category A), then dropped to 0.032 with IRI between 1.99 and 3.21 m/km (category B), followed by a slight decline to 0.030 with IRI between 3.21 and 6 m/km (category C). When the IRI was greater than 6 m/km (category D), the ANEF increased to 0.039. Driving on the pavement categorized to C and D may induce higher invehicle noise and driving stress indicated by drivers’ higher heart rates. Conclusion: The relationship between pavement roughness and vehicle emissions is nonlinear. The even smoother (category A) and even rougher (category D) road surfaces may also induce higher vehicle emissions. The proposed categorization of pavement roughness for Texas incorporates the impacts on environment and public health. To minimize the ANEF, the roughness in categories B and C (IRI: 2-6 m/km) is optimal for pavement design. If the impacts on in-vehicle noises and drivers’ heart rates are concerned as supplemental factors, category B (IRI: 1.99-3.21 m/km) is the best. Switching a pavement from category A to B, up to 34% of vehicle emissions and fuel consumption could be achieved. This categorization can be used in the design, maintenance, and evaluation of highway pavements, as well as applied to other states and countries with further calibrations of clusters for local specific classification of roughness.

Impacts of pavement types on in-vehicle noise and human health

ABSTRACT Noise is a major source of pollution that can affect the human physiology and living environment. According to the World Health Organization (WHO), an exposure for longer than 24 hours to noise levels above 70 dB(A) may damage human hearing sensitivity, induce adverse health effects, and cause anxiety to residents nearby roadways. Pavement type with different roughness is one of the associated sources that may contribute to in-vehicle noise. Most previous studies have focused on the impact of pavement type on the surrounding acoustic environment of roadways, and given little attention to in-vehicle noise levels. This paper explores the impacts of different pavement types on in-vehicle noise levels and the associated adverse health effects. An old concrete pavement and a pavement with a thin asphalt overlay were chosen as the test beds. The in-vehicle noise caused by the asphalt and concrete pavements were measured, as well as the drivers’ corresponding heart rates and reported riding comfort. Results show that the overall in-vehicle sound levels are higher than 70 dB(A) even at midnight. The newly overlaid asphalt pavement reduced in-vehicle noise at a driving speed of 96.5 km/hr by approximately 6 dB(A). Further, on the concrete pavement with higher roughness, driver heart rates were significantly higher than on the asphalt pavement. Drivers reported feeling more comfortable when driving on asphalt than on concrete pavement. Further tests on more drivers with different demographic characteristics, along highways with complicated configurations, and an examination of more factors contributing to in-vehicle noise are recommended, in addition to measuring additional physical symptoms of both drivers and passengers.Implications: While there have been many previous noise-related studies, few have addressed in-vehicle noise. Most studies have focused on the noise that residents have complained about, such as neighborhood traffic noise. As yet, there have been no complaints by drivers that their own in-vehicle noise is too loud. Nevertheless, it is a fact that in-vehicle noise can also result in adverse health effects if it exceeds 85 dB(A). Results of this study show that in-vehicle noise was strongly associated with pavement type and roughness; also, driver heart rate patterns presented statistically significant differences on different types of pavement with different roughness.

Fuzzy Evaluation of Contra-Flow Evacuation Plans in Texas Medical Center

With the needs of emergency preparedness due to various disasters such as Hurricane, flooding, radiological accident, terrorist attack, toxic material leakage, etc., an efficient transportation evacuation plan such as contra-flow is very crucial. Simulation is a superior tool that can measure the effects of different plans, however often offers contradictory Measurement of Effectiveness (MOE), which may not directly yield out an optimal evacuation plan. In this paper, Fuzzy Logic is employed to evaluate the evacuation plan. A case study illustrates the entire evacuation process that evacuates contra-flow evacuation plans in a typical multi-institutional area: Texas Medical Center in Houston, USA.