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Fang Zong
College of Transportation, Jilin University, Changchun 130022, PR China

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Journal article
Published: 28 May 2021 in Aerospace Science and Technology
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Flight departure delay prediction is one of the most critical components of intelligent aviation systems. The accurate prediction of flight departure delays can provide passengers with reliable travel schedules and enhance the service performance of airports and airlines. This article proposes a hybrid method of Random Forest Regression and Maximal Information Coefficient (RFR-MIC) for flight departure delay prediction. Random Forest Regression and Maximal Information Coefficient are inherently fused in terms of Information Consistency. Furthermore, this article focuses on utilizing flight information on multiple air routes for flight departure delay prediction. To validate the proposed flight departure delay prediction model, a numerical study is conducted using flight data collected from Beijing Capital International Airport (PEK). The proposed RFR-MIC model exhibits good performance compared with linear regression (LR), k-nearest neighbors (k-NN), artificial neural network (ANN), and standard Random Forest Regression (RFR). The results also show that flight information on multiple air routes can certainly improve the accuracy of flight departure delay prediction.

ACS Style

Zhen Guo; Bin Yu; Mengyan Hao; Wensi Wang; Yu Jiang; Fang Zong. A novel hybrid method for flight departure delay prediction using Random Forest Regression and Maximal Information Coefficient. Aerospace Science and Technology 2021, 116, 106822 .

AMA Style

Zhen Guo, Bin Yu, Mengyan Hao, Wensi Wang, Yu Jiang, Fang Zong. A novel hybrid method for flight departure delay prediction using Random Forest Regression and Maximal Information Coefficient. Aerospace Science and Technology. 2021; 116 ():106822.

Chicago/Turabian Style

Zhen Guo; Bin Yu; Mengyan Hao; Wensi Wang; Yu Jiang; Fang Zong. 2021. "A novel hybrid method for flight departure delay prediction using Random Forest Regression and Maximal Information Coefficient." Aerospace Science and Technology 116, no. : 106822.

Journal article
Published: 20 April 2021 in IEEE Access
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This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for fleet control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for fleet control in the case of CAVs’ homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.

ACS Style

Fang Zong; Meng Wang; Ming Tang; Xiying Li; Meng Zeng. An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles. IEEE Access 2021, 9, 66241 -66252.

AMA Style

Fang Zong, Meng Wang, Ming Tang, Xiying Li, Meng Zeng. An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles. IEEE Access. 2021; 9 ():66241-66252.

Chicago/Turabian Style

Fang Zong; Meng Wang; Ming Tang; Xiying Li; Meng Zeng. 2021. "An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles." IEEE Access 9, no. : 66241-66252.

Journal article
Published: 18 April 2021 in Physica A: Statistical Mechanics and its Applications
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Public transport networks (PTNs) undertake large amount of passenger demand in the urban transport system. Particularly, stations in metro networks with high significance in structure and function are more likely to contribute to passenger transport. Once these stations are under functional failure, it is easy to lead to the collapse of network connectivity. Thus, identifying critical nodes in PTNs is of practical significance for the public transport planning and operation. This study proposes an identification method for the critical nodes in multiplex network by considering the interaction between metro and bus networks. First, metro and bus networks are constructed by L-space and P-space methods, respectively. Then, ridership is extracted to describe the connection between nodes as the weights of links, and the metro-bus multiplex network is then constructed. Furthermore, the identification method in Multiplex Network based on Dempster–Shafer evidence theory (MNDS) is proposed to fuse the significance of nodes in sub-networks, and the critical nodes in the multiplex network are identified. Finally, the PTNs in Shenzhen City, China, is used as a case to demonstrate the feasibility of MNDS. By attacking critical nodes, a comparison is conducted and compared with two traditional identification methods (Weighted Closeness Centrality and Technique for Order Preference by Similarity to Ideal Solution) using two indicators, global efficiency (GE) and the size of the largest connected component (LCC). The results indicate the critical nodes identified by MNDS are of greater significance than those identified by the other two methods. This study provides a feasible method for critical nodes identification in urban public transport system, which can be applied to public transport operation and planning.

ACS Style

Jinjun Tang; Zhitao Li; Fan Gao; Fang Zong. Identifying critical metro stations in multiplex network based on D–S evidence theory. Physica A: Statistical Mechanics and its Applications 2021, 574, 126018 .

AMA Style

Jinjun Tang, Zhitao Li, Fan Gao, Fang Zong. Identifying critical metro stations in multiplex network based on D–S evidence theory. Physica A: Statistical Mechanics and its Applications. 2021; 574 ():126018.

Chicago/Turabian Style

Jinjun Tang; Zhitao Li; Fan Gao; Fang Zong. 2021. "Identifying critical metro stations in multiplex network based on D–S evidence theory." Physica A: Statistical Mechanics and its Applications 574, no. : 126018.

Journal article
Published: 15 March 2021 in Sustainability
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Path planning is one of the most important aspects for ambulance driving. A local dynamic path planning method based on the potential field theory is presented in this paper. The potential field model includes two components—repulsive potential and attractive potential. Repulsive potential includes road potential, lane potential and obstacle potential. Considering the driving distinction between an ambulance and a regular vehicle, especially in congested traffic, an adaptive potential function for a lane line is constructed in association with traffic conditions. The attractive potential is constructed with target potential, lane-velocity potential and tailgating potential. The design of lane-velocity potential is to characterize the influence of velocity on other lanes so as to prevent unnecessary lane-changing behavior for the sake of time-efficiency. The results obtained from simulation demonstrate that the proposed method yields a good performance for ambulance driving in an urban area, which can provide support for designing an ambulance support system for the ambulance personnel and dispatcher.

ACS Style

Fang Zong; Meng Zeng; Yang Cao; Yixuan Liu. Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field. Sustainability 2021, 13, 3194 .

AMA Style

Fang Zong, Meng Zeng, Yang Cao, Yixuan Liu. Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field. Sustainability. 2021; 13 (6):3194.

Chicago/Turabian Style

Fang Zong; Meng Zeng; Yang Cao; Yixuan Liu. 2021. "Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field." Sustainability 13, no. 6: 3194.

Journal article
Published: 20 October 2020 in IEEE Transactions on Intelligent Transportation Systems
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With the technological innovation, public transit has truly entered the era of big data. Bus operators have been possible to monitor the system conditions and obtain the real-time transit demand. This situation inspires us to rethink the transit demand prediction and real-time control problems. This article proposes a proactive real-time control method based on data-driven transit demand prediction with multi-source traffic data. A proactive control strategy is to predict the possible disturbance in the future by monitoring and inferring the system operation, and takes measures in advance to prevent the disturbance from disrupting the service regularity. Firstly, the further service reliability is assessed based on the evolution of the latest service reliabilities, to justify whether to conduct control actions. Secondly, if a control action is required, predict the transit demand and the number of alighting passengers. Thirdly, according to the predicted results, the bus dispatching time is optimized by minimizing passenger waiting time. A calculation process is introduced to solve the problem and the effectiveness of the proposed method is evaluated with the data of a real transit route. The results show that the control method based on transit demand prediction suits the needs of real-time control in the smart transit context.

ACS Style

Wensi Wang; Fang Zong; Baozhen Yao. A Proactive Real-Time Control Strategy Based on Data-Driven Transit Demand Prediction. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -13.

AMA Style

Wensi Wang, Fang Zong, Baozhen Yao. A Proactive Real-Time Control Strategy Based on Data-Driven Transit Demand Prediction. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-13.

Chicago/Turabian Style

Wensi Wang; Fang Zong; Baozhen Yao. 2020. "A Proactive Real-Time Control Strategy Based on Data-Driven Transit Demand Prediction." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-13.

Journal article
Published: 17 February 2020 in Sustainability
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Individual mobility patterns are an important factor in urban traffic planning and traffic flow forecasting. How to understand the spatio-temporal distribution of passengers deeply and accurately, so as to provide theoretical support for the planning and operation of the metro network, is an urgent issue of wide concern. In this paper, we applied NCP decomposition to uncover the characteristics of travel patterns from temporal and spatial dimensions in the metro network of Shenzhen City. Utilizing matrix factorization and correlation analysis, we extracted several stable components from the collective mobility and find that the departure and arrival mobility patterns have different characteristics in both the temporal and spatial dimension. According to the point of interest (POI) data in the Shenzhen City, the function attributes of the station are identified and then we found that the spatial distribution characteristics of different patterns are different. We explored the distribution of travel time classified according to the spatio-temporal characteristics of stable patterns. The proposed method can decompose stable travel patterns from the collective mobility and the results in this study can help us to better understand different mobility patterns in both spatial and temporal dimensions.

ACS Style

Jinjun Tang; Xiaolu Wang; Fang Zong; Zheng Hu. Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China. Sustainability 2020, 12, 1475 .

AMA Style

Jinjun Tang, Xiaolu Wang, Fang Zong, Zheng Hu. Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China. Sustainability. 2020; 12 (4):1475.

Chicago/Turabian Style

Jinjun Tang; Xiaolu Wang; Fang Zong; Zheng Hu. 2020. "Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China." Sustainability 12, no. 4: 1475.

Journal article
Published: 19 March 2019 in Physica A: Statistical Mechanics and its Applications
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Traffic flow prediction with high accuracy is definitely considered as one of most important parts in the Intelligent Transportation Systems. As interfering by some external factors, the raw traffic flow data containing noise may cause decline of prediction performance. This study proposes a prediction method by combining denoising schemes and support vector machine model to improve prediction accuracy. This study comprehensively evaluated the multi-step prediction performance of models with different denoising algorithms using the traffic volume data collected from three loop detectors located on highway in city of Minneapolis. In the prediction performance comparison, five denoising methods including EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition), MA (Moving Average), BW filter (Butterworth) and WL (Wavelet) are considered as candidates, specially, four wavelet types, coif (coiflet), db (daubechies), haar and sym (symlet), are further compared based on accuracy evaluation indicators. The prediction results show that the prediction results of the model combined with denoising algorithm are better that of the model without denoising strategy. Furthermore, the improvement of the EEMD on prediction performance is higher than other denoising algorithms, and WL method with db type achieves higher accuracy than other three types. Through comparing prediction accuracy of different denoising models, this study provides valuable suggestions for selecting the appropriate denoising approach for traffic flow prediction.

ACS Style

Jinjun Tang; Xinqiang Chen; Han Chunyang; Fang Zong; Chunyang Han; Leixiao Li. Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A: Statistical Mechanics and its Applications 2019, 534, 120642 .

AMA Style

Jinjun Tang, Xinqiang Chen, Han Chunyang, Fang Zong, Chunyang Han, Leixiao Li. Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A: Statistical Mechanics and its Applications. 2019; 534 ():120642.

Chicago/Turabian Style

Jinjun Tang; Xinqiang Chen; Han Chunyang; Fang Zong; Chunyang Han; Leixiao Li. 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes." Physica A: Statistical Mechanics and its Applications 534, no. : 120642.

Research article
Published: 28 April 2018 in International Journal of Distributed Sensor Networks
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Integration of urban and rural infrastructure is critical to integrating urban and rural public transport. A public transport hub is an important element of infrastructure, and it is the key facilities that serve as transferring points between cities and towns. The location of hub is related to the convenience of travel for urban and rural residents and the closeness of economic interactions between urban and rural areas. In this article, considering the background of the integration of urban and rural public transport, from the perspective of public transport hubs in urban and central town, a multi-level hub-and-spoke network is designed, and the location of integration of urban and rural public transport hub is determined. Based on the connection associated with central towns and the capacity constraints of hubs and to achieve the minimum total cost, this article proposes a mixed-integer programming model that employs a genetic and tabu search hybrid optimization algorithm to validate and analyze, which used the urban and rural public transport data from a specified area of Shandong province in China. The results indicate that the model can simultaneously determine locations for hubs in cities and central towns while minimizing total cost. The hub capacity constraint significantly influences the location of two-level hubs. The hub capacity constraint in the model can reduce the transportation cost for an entire network and optimize the transportation network. This study on urban and rural public transport hub location in a hub-and-spoke network not only reduces the transportation cost of the network but also completes and supplements the location theory of integration of urban and rural public transport.

ACS Style

Wei Zhong; Zhicai Juan; Fang Zong; Huishuang Su. Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport. International Journal of Distributed Sensor Networks 2018, 14, 1 .

AMA Style

Wei Zhong, Zhicai Juan, Fang Zong, Huishuang Su. Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport. International Journal of Distributed Sensor Networks. 2018; 14 (4):1.

Chicago/Turabian Style

Wei Zhong; Zhicai Juan; Fang Zong; Huishuang Su. 2018. "Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport." International Journal of Distributed Sensor Networks 14, no. 4: 1.

Journal article
Published: 06 April 2018 in IEEE Transactions on Intelligent Transportation Systems
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In this paper, we seek to identify the different impacts of external and internal information on taxis' cruising behaviors and to find effective methods to enhance taxis' cruising efficiency. Through global positioning system trajectory data collected in Shenzhen, China, we determine the impacts of external factors (land use, traffic conditions, and road grade) and internal factors (previous pick-up experience) on cruising location choice using a zero-inflated negative binomial model. The results indicate that the external factors have a more significant influence than the internal ones. Specifically, traffic conditions, land use, urban expressways, and previous pick-up points are the major factors that influence drivers' cruising decisions. The results also show that drivers follow different patterns during different times of day, i.e., relying on traffic conditions and land use in the morning/ evening peak hours but emphasizing land use and previous pick-up experience during non-peak hours. In addition, high-earning drivers and roaming drivers prefer to cruise in areas with high-density land use and optimal traffic conditions, whereas low-earning drivers and target drivers tend to cruise in areas with more previous pick-up points. These findings uncover the underlying mechanisms of cruising decisions and facilitate the development of strategies to minimize empty cruising time. Based on the study results, the external and internal information that was found to affect cruising decisions can be released to taxi drivers to improve their cruising efficiency. This information is also helpful to managers in deciding the overall layout of taxi service locations.

ACS Style

Fang Zong; Ting Wu; Hongfei Jia. Taxi Drivers’ Cruising Patterns—Insights from Taxi GPS Traces. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 571 -582.

AMA Style

Fang Zong, Ting Wu, Hongfei Jia. Taxi Drivers’ Cruising Patterns—Insights from Taxi GPS Traces. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (2):571-582.

Chicago/Turabian Style

Fang Zong; Ting Wu; Hongfei Jia. 2018. "Taxi Drivers’ Cruising Patterns—Insights from Taxi GPS Traces." IEEE Transactions on Intelligent Transportation Systems 20, no. 2: 571-582.

Journal article
Published: 14 December 2016 in Transportation Planning and Technology
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ACS Style

Fang Zong; Yixin Yuan; Jianfeng Liu; Yu Bai; Yanan He. Identifying travel mode with GPS data. Transportation Planning and Technology 2016, 40, 1 -14.

AMA Style

Fang Zong, Yixin Yuan, Jianfeng Liu, Yu Bai, Yanan He. Identifying travel mode with GPS data. Transportation Planning and Technology. 2016; 40 (2):1-14.

Chicago/Turabian Style

Fang Zong; Yixin Yuan; Jianfeng Liu; Yu Bai; Yanan He. 2016. "Identifying travel mode with GPS data." Transportation Planning and Technology 40, no. 2: 1-14.

Conference paper
Published: 01 August 2016 in Proceedings of the Institution of Civil Engineers - Transport
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A model system was developed to investigate the feasibility of an integrated transportation demand management (TDM) programme in the Nanhai district of China by employing the multi-nomial logit and nested logit modelling methods. Using the model system, the impacts of the programme concerning residents' acceptance and behaviour adjustments in trip frequency, mode choice, departure time and route choice are predicted. The study results indicate that the programme could make a significant contribution to the improvement of traffic conditions in Nanhai. Furthermore, it is recommended that implementation of the integrated TDM programme in Nanhai be divided into two stages – stage 1 for the implementation of bus priority and motorcycle restriction policies and stage 2 for implementing a congestion pricing policy. The study results may facilitate the establishment of related TDM programmes to aid the mitigation of traffic congestion and the sustainable development of urban transportation systems.

ACS Style

Fang Zong; Hongfei Jia; Zhicai Juan; Huiyong Zhang. Evaluating effects of integrated TDM measures in Nanhai, China. Proceedings of the Institution of Civil Engineers - Transport 2016, 169, 205 -218.

AMA Style

Fang Zong, Hongfei Jia, Zhicai Juan, Huiyong Zhang. Evaluating effects of integrated TDM measures in Nanhai, China. Proceedings of the Institution of Civil Engineers - Transport. 2016; 169 (4):205-218.

Chicago/Turabian Style

Fang Zong; Hongfei Jia; Zhicai Juan; Huiyong Zhang. 2016. "Evaluating effects of integrated TDM measures in Nanhai, China." Proceedings of the Institution of Civil Engineers - Transport 169, no. 4: 205-218.

Article
Published: 08 September 2015 in Journal of Central South University
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Taxi drivers’ cruising patterns are learnt with GPS trajectory data collected in Shenzhen, China. By employing Ripley’s K function, the impacts of land use and pick-up experience on taxis’ cruising behavior are investigated concerning about both intensity of influence and radius of influence. The results indicate that, in general, taxi drivers tend to learn more from land use characteristics than from pick-up experience. The optimal radius of influence of land use points and previous pick-up points is 14.18 km and 9.93 km, respectively. The findings also show that the high-earning drivers or thorough drivers pay more attention to land use characteristics and tend to cruise in high-density area, while the low-earning drivers or focus drivers prefer to learn more from previous pick-up experience and select the area which is far away from the high-density area. These findings facilitate the development of measures of managing taxi’s travel behavior by providing useful insights into taxis’ cruising patterns. The results also provide useful advice for taxi drivers to make efficient cruising decision, which will contribute to the improvement of cruising efficiency and the reduction of negative effects.

ACS Style

Fang Zong; Hui-Yong Zhang; Hai-Fan Li. Learning taxis’ cruising patterns with Ripley’s K function. Journal of Central South University 2015, 22, 3677 -3682.

AMA Style

Fang Zong, Hui-Yong Zhang, Hai-Fan Li. Learning taxis’ cruising patterns with Ripley’s K function. Journal of Central South University. 2015; 22 (9):3677-3682.

Chicago/Turabian Style

Fang Zong; Hui-Yong Zhang; Hai-Fan Li. 2015. "Learning taxis’ cruising patterns with Ripley’s K function." Journal of Central South University 22, no. 9: 3677-3682.

Journal article
Published: 17 July 2015 in Sustainability
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A key strategy of sustainable transportation, parking pricing can directly contribute to decreased greenhouse gas emissions and air pollution. This paper describes an optimal structure of parking rates in terms of parking locations and time of day. A two-level parking model based on game theory is established using parking survey data collected in Beijing in 2014. The model was estimated based on Stackelberg game and the Nash equilibrium. Using the two-level parking model, the optimal structure of parking rates for inside/outside business zones and during peak/off-peak hours was calculated. In addition, the relationship between the government (which represents the public benefit) and car users, as well as the relationships among car users in the parking system were investigated. The results indicate that equilibrium among all of the agents in the parking system can be obtained using the proposed parking rate structure. The findings provide a better understanding of parking behavior, and the two-level parking model presented in the paper can be used to determine the optimal parking rate to balance the temporal and spatial distribution of parking demand in urban areas. This research helps reduce car use and the parking-related cruising time and thus contributes to the reduction of carbon emissions and air pollution.

ACS Style

Fang Zong; Yanan He; Yixin Yuan. Dependence of Parking Pricing on Land Use and Time of Day. Sustainability 2015, 7, 9587 -9607.

AMA Style

Fang Zong, Yanan He, Yixin Yuan. Dependence of Parking Pricing on Land Use and Time of Day. Sustainability. 2015; 7 (7):9587-9607.

Chicago/Turabian Style

Fang Zong; Yanan He; Yixin Yuan. 2015. "Dependence of Parking Pricing on Land Use and Time of Day." Sustainability 7, no. 7: 9587-9607.

Articles
Published: 23 June 2015 in Transportation Planning and Technology
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In this paper, a Bayesian network is developed to investigate three intertwining parking decisions, namely parking period, parking location, and parking duration, and the impacts of a number of parking-related factors on these decisions. With parking information from Beijing, China in 2005, the structure and parameter of a Bayesian network were learnt by employing the K2 algorithm and Bayesian parameter estimation method respectively. The results show that the decision on how long to park follows that on where to park, and both of them are affected by the decision of when to park. This suggests that parking policies aimed at intervening in one specific parking decision may have an indirect influence on other parking decisions, which embraces an integrated view in the development of parking policies. The findings facilitate the development of measures for regulating parking behavior by identifying important contributing factors.

ACS Style

Fang Zong; Menglin Wang. Understanding parking decisions with a Bayesian network. Transportation Planning and Technology 2015, 38, 1 -16.

AMA Style

Fang Zong, Menglin Wang. Understanding parking decisions with a Bayesian network. Transportation Planning and Technology. 2015; 38 (6):1-16.

Chicago/Turabian Style

Fang Zong; Menglin Wang. 2015. "Understanding parking decisions with a Bayesian network." Transportation Planning and Technology 38, no. 6: 1-16.

Journal article
Published: 04 June 2015 in Information
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Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA’s contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices.

ACS Style

Fang Zong; Yu Bai; Xiao Wang; Yixin Yuan; Yanan He. Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm. Information 2015, 6, 212 -227.

AMA Style

Fang Zong, Yu Bai, Xiao Wang, Yixin Yuan, Yanan He. Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm. Information. 2015; 6 (2):212-227.

Chicago/Turabian Style

Fang Zong; Yu Bai; Xiao Wang; Yixin Yuan; Yanan He. 2015. "Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm." Information 6, no. 2: 212-227.

Article
Published: 08 August 2014 in Journal of Central South University
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The taxi drivers’ cruising pattern was learned using GPS trajectory data collected in Shenzhen, China. By employing zero-inflated Poisson model, the impacts of land use and previous pick-up experience on cruising decision were measured. The cruising strategies of different types of drivers as well as the top one driver were examined. The results indicate that both land use and previous pick-up experience affect travel behavior with the former’s influence (7.07×10−4 measured by one of the coefficients in zero-inflated Poisson model) being greater than the latter’s (4.58×10−t) in general, but the comparison also varies across the types of drivers. Besides, taxi drivers’ day-to-day learning feature is also proved by the results. According to comparison of the cruising behavior of the most efficient and inefficient driver, an efficient cruising strategy was proposed, that is, obeying the distribution of land use in choice of cruising area, while learning from pick-up experience in selection of detailed cruising location. By learning taxi drivers’ cruising pattern, the development of measures of regulating travel behaviors is facilitated, important factor for traffic organization and planning is identified, and an efficient cruising strategy for taxi drivers is provided.

ACS Style

Fang Zong. Understanding taxi driver’s cruising behavior with ZIP model. Journal of Central South University 2014, 21, 3404 -3410.

AMA Style

Fang Zong. Understanding taxi driver’s cruising behavior with ZIP model. Journal of Central South University. 2014; 21 (8):3404-3410.

Chicago/Turabian Style

Fang Zong. 2014. "Understanding taxi driver’s cruising behavior with ZIP model." Journal of Central South University 21, no. 8: 3404-3410.

Research article
Published: 30 October 2013 in Mathematical Problems in Engineering
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The paper presents a comparison between two modeling techniques, Bayesian network and Regression models, by employing them in accident severity analysis. Three severity indicators, that is, number of fatalities, number of injuries and property damage, are investigated with the two methods, and the major contribution factors and their effects are identified. The results indicate that the goodness of fit of Bayesian network is higher than that of Regression models in accident severity modeling. This finding facilitates the improvement of accuracy for accident severity prediction. Study results can be applied to the prediction of accident severity, which is one of the essential steps in accident management process. By recognizing the key influences, this research also provides suggestions for government to take effective measures to reduce accident impacts and improve traffic safety.

ACS Style

Fang Zong; Hongguo Xu; Huiyong Zhang. Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models. Mathematical Problems in Engineering 2013, 2013, 1 -9.

AMA Style

Fang Zong, Hongguo Xu, Huiyong Zhang. Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models. Mathematical Problems in Engineering. 2013; 2013 ():1-9.

Chicago/Turabian Style

Fang Zong; Hongguo Xu; Huiyong Zhang. 2013. "Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models." Mathematical Problems in Engineering 2013, no. : 1-9.

Journal article
Published: 01 June 2013 in Transport
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Staggered shifts is one of the popular TDM (Transportation Demand Management) policies, which can reduce commute travel volume during the AM and PM peak periods, and relieve traffic congestion. In order to make effective staggered shifts program, it is necessary to examine the effect of the program on commute travel behavior. This paper takes Beijing (China) as an example to evaluate the validity of staggered shifts policy. Based on data investigation, the commute travel behavior and the commuters’ preference for staggered shifts are analyzed. This paper makes four staggered shifts programs, and develops a commute departure time choice model with Multinomial Logit method to predict the influence of the programs on commute departure time, and develops a commute travel duration model to analyze the influence of the programs on commute travel time. Departure time prediction shows that Program B can reduce the traffic volumes in 6:30–8:30 period by 15.24%, and commute travel duration analysis indicate that Program B can reduce the home-to-work travel time by 21.73%. Therefore, Program B is proven to be the best staggered shifts program for Beijing. The results of this paper can provide valuable information on how to develop an effective staggered shifts program.

ACS Style

Fang Zong; Zhicai Juan; Hongfei Jia. Examination of staggered shifts impacts on travel behavior: a case study of Beijing, China. Transport 2013, 28, 175 -185.

AMA Style

Fang Zong, Zhicai Juan, Hongfei Jia. Examination of staggered shifts impacts on travel behavior: a case study of Beijing, China. Transport. 2013; 28 (2):175-185.

Chicago/Turabian Style

Fang Zong; Zhicai Juan; Hongfei Jia. 2013. "Examination of staggered shifts impacts on travel behavior: a case study of Beijing, China." Transport 28, no. 2: 175-185.

Conference paper
Published: 22 July 2010 in ICCTP 2010
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Urban rail transit has a great impact on land use. Models that define rail transit affected area and value added to local real estate are studied by the analysis of effects that rail transit has on land use and pricing of real estate. After comparing the results of different models, the proper transit influenced regions of Light rail line 3 in Changchun city are defined, and the value added to local real estate along rail transit is calculated with a half logarithm model of Hedonic Price Model (HPM). In conclusion, as the results show, rail transit has a positive effect to Changchun city, though it has problems like high investment, long-term construction, and noise pollution. The research is helpful to relevant government departments in decision making, promoting the comprehensive development of the land around rail transit stations, and reducing investment and management pressure on the rail transit system.

ACS Style

Hongfei Jia; Fang Zong; Miao Zhang. Impact on Land Use and Local Real Estate along the Urban Rail Transit. ICCTP 2010 2010, 1 .

AMA Style

Hongfei Jia, Fang Zong, Miao Zhang. Impact on Land Use and Local Real Estate along the Urban Rail Transit. ICCTP 2010. 2010; ():1.

Chicago/Turabian Style

Hongfei Jia; Fang Zong; Miao Zhang. 2010. "Impact on Land Use and Local Real Estate along the Urban Rail Transit." ICCTP 2010 , no. : 1.

Conference paper
Published: 05 January 2009 in Logistics
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Reasonable organization of transfer traffic is the basic guarantee to realize the effective joint of different traffic ways in integrated passenger transport terminals, to shorten the transfer time of passengers, and to raise the distribution efficiency of transport terminals and the entire service level of passenger transport systems. Based on the analysis of the characteristics and service scope of different traffic ways, the impossible transfer modes among different traffic ways were pointed out, and a forecasting model of transfer volume was developed by using aggregating method and referring to the theory of Gravity Model. Meanwhile, reasonable organization method of vehicle flow and passenger flow was put forward from different aspects, such as, reasonable disposition of transport faculties, real time control and coordinative dispatch of vehicles, efficient signage guide and the establishment of passenger information service system. The result has reference value to the design and implement of the organization scheme for the urban integrated passenger transport terminals.

ACS Style

Hongfei Jia; Xinxin Xing; Fang Zong. The Study of Traffic Organization in Transport Terminals. Logistics 2009, 1 .

AMA Style

Hongfei Jia, Xinxin Xing, Fang Zong. The Study of Traffic Organization in Transport Terminals. Logistics. 2009; ():1.

Chicago/Turabian Style

Hongfei Jia; Xinxin Xing; Fang Zong. 2009. "The Study of Traffic Organization in Transport Terminals." Logistics , no. : 1.