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Real-time traffic speed information is essential for effective traffic management. Due to the high costs of conventional data collection methods, transportation agencies have sought alternative data sources, such as Waze. To thoroughly evaluate and explore Waze, or similar probe-based traffic data, This study used a 10.8 mile stretch of I-40 in Knoxville, Tennessee to compare the speed measurements from Waze and Remote Traffic Microwave Sensors (RTMS) over a 2-month period. The main findings include: 1) The two independent datasets exhibit similar speed patterns and profiles with Waze speed values being slight higher than RTMS speeds under high-speed lower traffic conditions; the Waze speed values are more likely to be similar or even lower than RTMS speeds for low-speed near congestion conditions; 2) several factors affecting the speed differences between RTMS speeds and Waze speeds were identified, such as Waze speed value, time of day (peak vs. non-peak), AADT (Annual Average Daily Traffic), and segment length; and 3) Waze reported the same speed for several successive reporting periods if the real-time speed is not available. The effective Waze sampling period is about two to four minutes. Waze speeds had more real-time speed observations during congested times, indicating that Waze speeds are more reliable for congested scenarios. The findings lead to a better understanding of this source of data and the subsequent analysis results.
Zhihua Zhang; Lee D. Han; Yuandong Liu. Exploration and evaluation of crowdsourced probe-based Waze traffic speed. Transportation Letters 2021, 1 -9.
AMA StyleZhihua Zhang, Lee D. Han, Yuandong Liu. Exploration and evaluation of crowdsourced probe-based Waze traffic speed. Transportation Letters. 2021; ():1-9.
Chicago/Turabian StyleZhihua Zhang; Lee D. Han; Yuandong Liu. 2021. "Exploration and evaluation of crowdsourced probe-based Waze traffic speed." Transportation Letters , no. : 1-9.
In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to monitor freeways. The Highway Capacity Manual (HCM) provides analytical methods to assess LOS based on traffic density and highway characteristics. Generally, obtaining reliable density data on every road in large networks using traditional fixed location sensors and cameras is expensive and otherwise unrealistic. Traditional intelligent transportation system facilities are typically limited to major urban areas in different states. Crowdsourced data are an emerging, low-cost solution that can potentially improve safety and operations. This study incorporates crowdsourced data provided by Waze to propose an algorithm for LOS assessment on an hourly basis. The proposed algorithm exploits various features from big data (crowdsourced Waze user alerts and speed/travel time variation) to perform LOS classification using machine learning models. Three categories of model inputs are introduced: Basic statistical measures of speed; travel time reliability measures; and the number of hourly Waze alerts. Data collected from fixed location sensors were used to calculate ground truth LOS. The results reveal that using Waze crowdsourced alerts can improve the LOS estimation accuracy by about 10% (accuracy = 0.93, Kappa = 0.83). The proposed method was also tested and confirmed by using data from after coronavirus disease 2019 (COVID-19) with severe traffic breakdown due to a stay-at-home policy. The proposed method is extendible for freeways in other locations. The results of this research provide transportation agencies with a LOS method based on crowdsourced data on different freeway segments, regardless of the availability of traditional fixed location sensors.
Nima Hoseinzadeh; Yangsong Gu; Lee Han; Candace Brakewood; Phillip Freeze. Estimating Freeway Level-of-Service Using Crowdsourced Data. Informatics 2021, 8, 17 .
AMA StyleNima Hoseinzadeh, Yangsong Gu, Lee Han, Candace Brakewood, Phillip Freeze. Estimating Freeway Level-of-Service Using Crowdsourced Data. Informatics. 2021; 8 (1):17.
Chicago/Turabian StyleNima Hoseinzadeh; Yangsong Gu; Lee Han; Candace Brakewood; Phillip Freeze. 2021. "Estimating Freeway Level-of-Service Using Crowdsourced Data." Informatics 8, no. 1: 17.
Obtaining accurate speed and travel time information is a challenge for researchers, geographers, and transportation agencies. In the past, traffic data were usually acquired and disseminated by government agencies through fixed-location sensors. High costs, infrastructure demands, and low coverage levels of these sensor devices require agencies and researchers to look beyond the traditional approaches. With the emergence of smartphones and navigation apps, location-based and crowdsourced Big Data are receiving increased attention. In this regard, location-based big data (LocBigData) collected from probe vehicles and road users can be used to provide speed and travel time information in different locations. Examining the quality of crowdsourced data is essential for researchers and agencies before using them. This study assessed the quality of Waze speed data from surface streets and conducted a case study in Sevierville, Tennessee. Typically, examining the quality of these data in surface streets and arterials is more challenging than freeways data. This research used Bluetooth speed data as the ground truth, which is independent of Waze data. In this study, three steps of methodology were used. In the first step, Waze speed data was compared to Bluetooth data in terms of accuracy, mean difference, and distribution similarity. In the second step, a k-means algorithm was used to categorize Waze data quality, and a multinomial logistics regression model was performed to explore the significant factors that impact data quality. Finally, in the third step, machine learning techniques were conducted to predict the data quality in different conditions. The result of the comparison showed a similar pattern and a slight difference between datasets, which verified the quality of Waze speed data. The statistical model indicates that that Waze speed data are more accurate in peak hours than in night hours. Also, the traffic speed, traffic volume, and segment length have a significant association on the accuracy of Waze data on surface streets. Finally, the result of machine learning prediction showed that a KNN method performed the highest prediction accuracy of 84.5% and 82.9% of the time for training and test datasets, respectively. Overall, the study results suggest that Waze speed data is a promising data source for surface streets.
Nima Hoseinzadeh; Yuandong Liu; Lee D. Han; Candace Brakewood; Amin Mohammadnazar. Quality of location-based crowdsourced speed data on surface streets: A case study of Waze and Bluetooth speed data in Sevierville, TN. Computers, Environment and Urban Systems 2020, 83, 101518 .
AMA StyleNima Hoseinzadeh, Yuandong Liu, Lee D. Han, Candace Brakewood, Amin Mohammadnazar. Quality of location-based crowdsourced speed data on surface streets: A case study of Waze and Bluetooth speed data in Sevierville, TN. Computers, Environment and Urban Systems. 2020; 83 ():101518.
Chicago/Turabian StyleNima Hoseinzadeh; Yuandong Liu; Lee D. Han; Candace Brakewood; Amin Mohammadnazar. 2020. "Quality of location-based crowdsourced speed data on surface streets: A case study of Waze and Bluetooth speed data in Sevierville, TN." Computers, Environment and Urban Systems 83, no. : 101518.
The authors would like to add the following paragraphs in the article note section of the original version of the paper:
Wei Lu; Lee D. Han; Cheng Liu; Budhendra L. Bhaduri. Correction to: Impacts of High Resolution Data on Traveler Compliance Levels in Emergency Evacuation Simulations. International Journal of Intelligent Transportation Systems Research 2020, 18, 461 -461.
AMA StyleWei Lu, Lee D. Han, Cheng Liu, Budhendra L. Bhaduri. Correction to: Impacts of High Resolution Data on Traveler Compliance Levels in Emergency Evacuation Simulations. International Journal of Intelligent Transportation Systems Research. 2020; 18 (3):461-461.
Chicago/Turabian StyleWei Lu; Lee D. Han; Cheng Liu; Budhendra L. Bhaduri. 2020. "Correction to: Impacts of High Resolution Data on Traveler Compliance Levels in Emergency Evacuation Simulations." International Journal of Intelligent Transportation Systems Research 18, no. 3: 461-461.
Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.
Kejun Long; Wukai Yao; Jian Gu; Wei Wu; Lee D. Han. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Applied Sciences 2018, 9, 104 .
AMA StyleKejun Long, Wukai Yao, Jian Gu, Wei Wu, Lee D. Han. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Applied Sciences. 2018; 9 (1):104.
Chicago/Turabian StyleKejun Long; Wukai Yao; Jian Gu; Wei Wu; Lee D. Han. 2018. "Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration." Applied Sciences 9, no. 1: 104.
The mechanisms of traffic congestion generation are more than complicated, due to complex geometric road designs and complicated driving behavior at urban expressways in China. We employ a cell transmission model (CTM) to simulate the traffic flow spatiotemporal evolution process along the expressway, and reveal the characteristics of traffic congestion occurrence and propagation. Here, we apply the variable-length-cell CTM to adapt the complicated road geometry and configuration, and propose the merge section CTM considering drivers’ mandatory lane-changing and other unreasonable behavior at the on-ramp merge section, and propose the diverge section CTM considering queue length end extending the expressway mainline to generate a dynamic bottleneck at the diverge section. In the new improved CTM model, we introduce merge ratio and diverge ratio to describe the effect of driver behavior at the merge and diverge section. We conduct simulations on the real urban expressway in China, with results showing that the merge section and diverge section are the original location of expressway traffic congestion generation, and on/off-ramp traffic flow has a great effect on the expressway mainline operation. When on-ramp traffic volume increases by 40%, the merge section delay increases by 35%, and when off-ramp capacity increases by 100 veh/hr, the diverge section delay decreases about by 10%, which proves the strong interaction between expressway and adjacent road networks. Our results provide the underlying insights of traffic congestion mechanism in urban expressway in China, which can be used to better understand and manage this issue.
Kejun Long; Qin Lin; Jian Gu; Wei Wu; Lee D. Han. Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China. Sustainability 2018, 10, 4359 .
AMA StyleKejun Long, Qin Lin, Jian Gu, Wei Wu, Lee D. Han. Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China. Sustainability. 2018; 10 (12):4359.
Chicago/Turabian StyleKejun Long; Qin Lin; Jian Gu; Wei Wu; Lee D. Han. 2018. "Exploring Traffic Congestion on Urban Expressways Considering Drivers’ Unreasonable Behavior at Merge/Diverge Sections in China." Sustainability 10, no. 12: 4359.
With rapid economic growth, previously less populated peripheral areas of major cities in China have now become densely populated. Many hazardous installations (e.g., chemical plants, tank farms, depots of explosive materials, etc.) that used to be in these less populated areas are now found in the backyards of millions of citizens going about their daily lives with minimal knowledge about the dangers these installations pose. A hazard mishap that may have been containable with no human loss just 30 years ago can now threaten the lives of millions. While numerous studies have assessed hazard-specific risks, few efforts have attempted to establish a comprehensive framework to assess multi-hazard scenarios. For this purpose, this study developed a framework for spatial risk assessment of multiple hazardous installations in a metropolitan area. The framework includes three quantitative models for the analysis of the inherent danger associated with, the accessibility for rescue operations at, and the potential effectiveness of mass evacuation from these hazardous installations. Based on this framework and its quantitative models, a case study of the city of Beijing was conducted in this research.
Qiansheng Zhao; Lee D. Han; Nianxue Luo. A proposed semi-quantitative framework for comprehensive risk assessment of urban hazard installations considering rescue accessibility and evacuation vulnerability. Safety Science 2018, 110, 192 -203.
AMA StyleQiansheng Zhao, Lee D. Han, Nianxue Luo. A proposed semi-quantitative framework for comprehensive risk assessment of urban hazard installations considering rescue accessibility and evacuation vulnerability. Safety Science. 2018; 110 ():192-203.
Chicago/Turabian StyleQiansheng Zhao; Lee D. Han; Nianxue Luo. 2018. "A proposed semi-quantitative framework for comprehensive risk assessment of urban hazard installations considering rescue accessibility and evacuation vulnerability." Safety Science 110, no. : 192-203.
Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.
Hongtai Yang; Jianjiang Yang; Lee D. Han; Xiaohan Liu; Li Pu; Shih-Miao Chin; Ho-Ling Hwang. A Kriging based spatiotemporal approach for traffic volume data imputation. PLOS ONE 2018, 13, e0195957 .
AMA StyleHongtai Yang, Jianjiang Yang, Lee D. Han, Xiaohan Liu, Li Pu, Shih-Miao Chin, Ho-Ling Hwang. A Kriging based spatiotemporal approach for traffic volume data imputation. PLOS ONE. 2018; 13 (4):e0195957.
Chicago/Turabian StyleHongtai Yang; Jianjiang Yang; Lee D. Han; Xiaohan Liu; Li Pu; Shih-Miao Chin; Ho-Ling Hwang. 2018. "A Kriging based spatiotemporal approach for traffic volume data imputation." PLOS ONE 13, no. 4: e0195957.
Bumjoon Bae; Hyun Kim; Hyeonsup Lim; Yuandong Liu; Lee D. Han; Phillip B. Freeze. Missing data imputation for traffic flow speed using spatio-temporal cokriging. Transportation Research Part C: Emerging Technologies 2018, 88, 124 -139.
AMA StyleBumjoon Bae, Hyun Kim, Hyeonsup Lim, Yuandong Liu, Lee D. Han, Phillip B. Freeze. Missing data imputation for traffic flow speed using spatio-temporal cokriging. Transportation Research Part C: Emerging Technologies. 2018; 88 ():124-139.
Chicago/Turabian StyleBumjoon Bae; Hyun Kim; Hyeonsup Lim; Yuandong Liu; Lee D. Han; Phillip B. Freeze. 2018. "Missing data imputation for traffic flow speed using spatio-temporal cokriging." Transportation Research Part C: Emerging Technologies 88, no. : 124-139.
Bumjoon Bae; Brandon C. Whetsel; Lee D. Han. Gray Areas in Isolated Intersection Control-Type Selection: Complementary Decision-Support Tool. Journal of Transportation Engineering, Part A: Systems 2017, 143, 04017055 .
AMA StyleBumjoon Bae, Brandon C. Whetsel, Lee D. Han. Gray Areas in Isolated Intersection Control-Type Selection: Complementary Decision-Support Tool. Journal of Transportation Engineering, Part A: Systems. 2017; 143 (11):04017055.
Chicago/Turabian StyleBumjoon Bae; Brandon C. Whetsel; Lee D. Han. 2017. "Gray Areas in Isolated Intersection Control-Type Selection: Complementary Decision-Support Tool." Journal of Transportation Engineering, Part A: Systems 143, no. 11: 04017055.
Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.
Zhihua Zhang; Rachel J. C. Chen; Lee D. Han; Lu Yang. Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach. Sustainability 2017, 9, 1635 .
AMA StyleZhihua Zhang, Rachel J. C. Chen, Lee D. Han, Lu Yang. Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach. Sustainability. 2017; 9 (9):1635.
Chicago/Turabian StyleZhihua Zhang; Rachel J. C. Chen; Lee D. Han; Lu Yang. 2017. "Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach." Sustainability 9, no. 9: 1635.
Minghua Zeng; Kejun Long; Lee D. Han; Xiaoguang Yang. Optimization of Degradable Road Network Considering VMS Information and Heterogeneous ATIS Users. Journal of Computing in Civil Engineering 2017, 31, 04017048 .
AMA StyleMinghua Zeng, Kejun Long, Lee D. Han, Xiaoguang Yang. Optimization of Degradable Road Network Considering VMS Information and Heterogeneous ATIS Users. Journal of Computing in Civil Engineering. 2017; 31 (5):04017048.
Chicago/Turabian StyleMinghua Zeng; Kejun Long; Lee D. Han; Xiaoguang Yang. 2017. "Optimization of Degradable Road Network Considering VMS Information and Heterogeneous ATIS Users." Journal of Computing in Civil Engineering 31, no. 5: 04017048.
Shuguang Ji; Christopher R. Cherry; Lee D. Han; David A. Jordan. Electric bike sharing: simulation of user demand and system availability. Journal of Cleaner Production 2014, 85, 250 -257.
AMA StyleShuguang Ji, Christopher R. Cherry, Lee D. Han, David A. Jordan. Electric bike sharing: simulation of user demand and system availability. Journal of Cleaner Production. 2014; 85 ():250-257.
Chicago/Turabian StyleShuguang Ji; Christopher R. Cherry; Lee D. Han; David A. Jordan. 2014. "Electric bike sharing: simulation of user demand and system availability." Journal of Cleaner Production 85, no. : 250-257.
Simulation-based study is one of the major methods for evacuation planning. How to quickly build an origin-destination (OD) matrix from each source zone to their nearest destination becomes an issue. In this paper, we propose a new problem - Multiple-Source, Nearest Destination, Shortest Path (MSNDSP) - for generating an OD matrix in evacuation assignments. Compared to a benchmark study using Dijkstra's algorithm, we propose a new Super Node-based Trip Generator (SNTG) algorithm to improve the computing performance. The new algorithm significantly reduces the computational time through transforming the MSNDSP problem to a normal single-source, shortest path problem with a super-node concept. Experimental studies using real-world street networks and high-resolution LandScan USA population data indicate that the SNTG algorithm can provide OD output identical to the benchmark study, but the computing time is about 500 to 45,000 times faster in different network sizes. Discussion of this algorithm in other applications is also conducted.
Wei Lu; Lee D. Han; Cheng Liu; Kejun Long. A Multiple-Source, Nearest Destination, Shortest Path Problem in Evacuation Assignments. CICTP 2014 2014, 3691 -3702.
AMA StyleWei Lu, Lee D. Han, Cheng Liu, Kejun Long. A Multiple-Source, Nearest Destination, Shortest Path Problem in Evacuation Assignments. CICTP 2014. 2014; ():3691-3702.
Chicago/Turabian StyleWei Lu; Lee D. Han; Cheng Liu; Kejun Long. 2014. "A Multiple-Source, Nearest Destination, Shortest Path Problem in Evacuation Assignments." CICTP 2014 , no. : 3691-3702.
Chunjiao Dong; Chunfu Shao; Stephen H. Richards; Lee D. Han. Flow rate and time mean speed predictions for the urban freeway network using state space models. Transportation Research Part C: Emerging Technologies 2014, 43, 20 -32.
AMA StyleChunjiao Dong, Chunfu Shao, Stephen H. Richards, Lee D. Han. Flow rate and time mean speed predictions for the urban freeway network using state space models. Transportation Research Part C: Emerging Technologies. 2014; 43 ():20-32.
Chicago/Turabian StyleChunjiao Dong; Chunfu Shao; Stephen H. Richards; Lee D. Han. 2014. "Flow rate and time mean speed predictions for the urban freeway network using state space models." Transportation Research Part C: Emerging Technologies 43, no. : 20-32.
Automated enforcement red-light cameras (RLC) have been widely adopted by municipalities around the world as a measure of curbing red-light running (RLR) at signalized intersections and reducing the cost of law enforcement. While a consensus has not yet been reached about whether RLC in general can benefit intersection safety by reducing RLR and crashes, recent debates revolve around using RLC as a revenue generator. Some of the political backlash of RLC is the perception that they are installed primarily to fulfill revenue guarantees and sustain the RLC program. Some municipalities have been charged with changing the signal phasing to trap more red-light runners and increase the revenue from RLC programs. This paper focuses on a number of engineering strategies, mainly related to signal timing that may be used by municipalities to achieve their financial goals. The negative impacts of implementing these measures on the safety and efficiency of intersection operations and public support on RLC programs are also discussed. These strategies are also revealed to increase transparency of the divergent motivations of RLC vendors, municipalities, policy makers and safety advocates.
Qiang Yang; Lee D. Han; Christopher R. Cherry. Some measures for sustaining red-light camera programs and their negative impacts. Transport Policy 2013, 29, 192 -198.
AMA StyleQiang Yang, Lee D. Han, Christopher R. Cherry. Some measures for sustaining red-light camera programs and their negative impacts. Transport Policy. 2013; 29 ():192-198.
Chicago/Turabian StyleQiang Yang; Lee D. Han; Christopher R. Cherry. 2013. "Some measures for sustaining red-light camera programs and their negative impacts." Transport Policy 29, no. : 192-198.
License plate recognition (LPR) technology is a mature yet imperfect technology used for automated toll collection and speed enforcement. The portion of license plates that can be correctly recognized and matched at two separate stations is typically in the range of 35% or less. Existing methods for improving the matching of plates recognized by LPR units rely on intensive manual data reduction, such that the misread plates are manually entered into the system. Recently, an advanced matching technique that combines Bayesian probability and Levenshtein text-mining techniques was proposed to increase the accuracy of automated license plate matching. The key component of this method is what we called the association matrix, which contains the conditional probabilities of observing one character at one station for a given observed character at another station. However, the estimation of the association matrix relies on the manually extracted ground truth of a large number of plates, which is a cumbersome and tedious process. To overcome this drawback, in this study, we propose an ingenious novel self-learning algorithm that eliminates the need for extracting ground truth manually. These automatically learned association matrices are found to perform well in the correctness in plate matching, in comparison with those generated from the painstaking manual method. Furthermore, these automatically learned association matrices outperform their manual counterparts in reducing false matching rates. The automatic self-learning method is also cheaper and easier to implement and continues to improve and correct itself over time.
Francisco Moraes Oliveira Neto; Lee D. Han; Myong K Jeong. An Online Self-Learning Algorithm for License Plate Matching. IEEE Transactions on Intelligent Transportation Systems 2013, 14, 1806 -1816.
AMA StyleFrancisco Moraes Oliveira Neto, Lee D. Han, Myong K Jeong. An Online Self-Learning Algorithm for License Plate Matching. IEEE Transactions on Intelligent Transportation Systems. 2013; 14 (4):1806-1816.
Chicago/Turabian StyleFrancisco Moraes Oliveira Neto; Lee D. Han; Myong K Jeong. 2013. "An Online Self-Learning Algorithm for License Plate Matching." IEEE Transactions on Intelligent Transportation Systems 14, no. 4: 1806-1816.
This paper presents a neurofuzzy signal control system to improve the efficiency at closely-spaced signalized intersections. Building on the conventional actuated-coordinated control system, the neurofuzzy controller establishes a “secondary coordination” between the upstream coordinated phase and the downstream non-coordinated phase based on real-time traffic demand. Under the neurofuzzy signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left turn lane and be served, and therefore reduce the possibility of being delayed at the downstream intersection. The membership functions in the fuzzy controller are calibrated to further the performance. The simulation results indicate that the neurofuzzy signal control consistently outperformed to the conventional actuated-coordinated controller, in terms of reduction in system-wide average delay and average number of stops per vehicle, under a wide range of traffic volumes, especially under higher demand conditions.
Xiao Li Sun; Tom Urbanik; Lee D. Han. Neurofuzzy Control to Actuated-Coordinated System at Closely-Spaced Intersections. Applied Mechanics and Materials 2013, 321-324, 1249 -1258.
AMA StyleXiao Li Sun, Tom Urbanik, Lee D. Han. Neurofuzzy Control to Actuated-Coordinated System at Closely-Spaced Intersections. Applied Mechanics and Materials. 2013; 321-324 ():1249-1258.
Chicago/Turabian StyleXiao Li Sun; Tom Urbanik; Lee D. Han. 2013. "Neurofuzzy Control to Actuated-Coordinated System at Closely-Spaced Intersections." Applied Mechanics and Materials 321-324, no. : 1249-1258.
The speed limit of 55 mph (88 km/h) is used typically on rural highways in the U.S. When curbs are installed, a lower speed limit is suggested because running into curbs at high speeds may cause significant vehicular damage and severe injuries. However, it has been argued that lowering the speed limit may cause confusion in drivers, who do not perceive the risk and tend to operate their vehicles at the same speed as before. To better understand driver behaviour on two-lane rural highways before and after curb installation, the authors conducted a series of experiments on a high-fidelity driving simulator in different posted speed limit, curb installation, lateral curb clearance, weather, visibility, and traffic conditions. Results of the study suggest that driver behaviours are influenced by the various factors in a complex and interrelated manner. It is likely that curbs have no influence on a driver's selection of speed. Drivers do perceive the risk from the curb or the opposing traffic when selecting their lane positions. The available space between the curb and the opposing traffic is crucial and has significant effects on driving behaviours. The subjective effects of drivers are found to be influential to driving behaviours.
Qiang Yang; Ryan Overton; Lee D. Han; Xuedong Yan; Stephen H. Richards. Driver behaviours on rural highways with and without curbs – a driving simulator based study. International Journal of Injury Control and Safety Promotion 2013, 21, 115 -126.
AMA StyleQiang Yang, Ryan Overton, Lee D. Han, Xuedong Yan, Stephen H. Richards. Driver behaviours on rural highways with and without curbs – a driving simulator based study. International Journal of Injury Control and Safety Promotion. 2013; 21 (2):115-126.
Chicago/Turabian StyleQiang Yang; Ryan Overton; Lee D. Han; Xuedong Yan; Stephen H. Richards. 2013. "Driver behaviours on rural highways with and without curbs – a driving simulator based study." International Journal of Injury Control and Safety Promotion 21, no. 2: 115-126.
Kejun Long; Yue Liu; Lee D. Han. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science 2013, 54, 8 -16.
AMA StyleKejun Long, Yue Liu, Lee D. Han. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science. 2013; 54 ():8-16.
Chicago/Turabian StyleKejun Long; Yue Liu; Lee D. Han. 2013. "Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China." Safety Science 54, no. : 8-16.