This page has only limited features, please log in for full access.
In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.
Jian Gu; Miaohua Li; Linghua Yu; Shun Li; Kejun Long. Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data. Journal of Advanced Transportation 2021, 2021, 1 -19.
AMA StyleJian Gu, Miaohua Li, Linghua Yu, Shun Li, Kejun Long. Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data. Journal of Advanced Transportation. 2021; 2021 ():1-19.
Chicago/Turabian StyleJian Gu; Miaohua Li; Linghua Yu; Shun Li; Kejun Long. 2021. "Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data." Journal of Advanced Transportation 2021, no. : 1-19.
Radio Frequency Identification (RFID) plays an important role on road traffic management and basic data could be collected from automotive electronic registration identifications, which could be applied for road traffic management and planning. Firstly, road section volume-time function was discussed in the paper, and then urban road RFID data collected from the city of Nanjing were analyzed with mature methods on identifying redundant and wrong data based on license plate number. Then the function from Bureau of Public Road (BPR) was calibrated to get the basic volume-time function with RFID data, and the basic function was improved based on the time periods, and the improved functions were used to describe relationships between volume and travel time in weekdays and weekends, and the fitting effect is compared with the basic function. It was indicated that the fitting effort R2 was increased by 40% at least. The results can provide reference and support for urban road traffic planning.
Jian Gu; Linghua Yu; Miaohua Li. Application Study on Urban Road Volume-Time Function Based on RFID Data. IOP Conference Series: Materials Science and Engineering 2020, 787, 1 .
AMA StyleJian Gu, Linghua Yu, Miaohua Li. Application Study on Urban Road Volume-Time Function Based on RFID Data. IOP Conference Series: Materials Science and Engineering. 2020; 787 ():1.
Chicago/Turabian StyleJian Gu; Linghua Yu; Miaohua Li. 2020. "Application Study on Urban Road Volume-Time Function Based on RFID Data." IOP Conference Series: Materials Science and Engineering 787, no. : 1.
Metros are usually built and added on the basis of a completed bus network in Chinese cities. After the metro construction, it is faced with the problem of how to adjust and optimize the original bus lines based on the new metro system. This research mainly proposes a bus line optimization method based on bus and metro integration. In the consideration of the geographical space, the cooperation and competition relationship between bus and metro lines is qualitatively introduced according to the geographical location and service range of metro (800 m radius) and bus (500 m radius) stations. The competition and cooperation indexes are applied to define the co-opetition relationship between bus and metro lines. The bus line optimization model is constructed based on the co-opetition coefficient and Changsha Metro Line Number 2 is chosen as a case study to verify the optimization model. The results show that the positive competition, efficient cooperation, and travel efficiency between metro and bus has been significantly enhanced after optimization. Moreover, this paper provides a reasonable reference for public transport network planning and resource allocation.
Junjun Wei; Kejun Long; Jian Gu; Qingling Ju; Piao Zhu. Optimizing Bus Line Based on Metro-Bus Integration. Sustainability 2020, 12, 1493 .
AMA StyleJunjun Wei, Kejun Long, Jian Gu, Qingling Ju, Piao Zhu. Optimizing Bus Line Based on Metro-Bus Integration. Sustainability. 2020; 12 (4):1493.
Chicago/Turabian StyleJunjun Wei; Kejun Long; Jian Gu; Qingling Ju; Piao Zhu. 2020. "Optimizing Bus Line Based on Metro-Bus Integration." Sustainability 12, no. 4: 1493.
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.
Jian Gu; Shuyan Chen. Urban expressway density analysis based on the cusp catastrophe theory. Advances in Civil Engineering and Building Materials 2015, 405 -408.
AMA StyleJian Gu, Shuyan Chen. Urban expressway density analysis based on the cusp catastrophe theory. Advances in Civil Engineering and Building Materials. 2015; ():405-408.
Chicago/Turabian StyleJian Gu; Shuyan Chen. 2015. "Urban expressway density analysis based on the cusp catastrophe theory." Advances in Civil Engineering and Building Materials , no. : 405-408.
We applied catastrophe and chaos theory to analyze the traffic nonlinear characteristics of expressway condition. Catastrophe theory was generally used to explore the mathematical relationships among the traffic data collected from highway conditions, which could not be appropriate for the urban expressway conditions. Traffic flow data collected from the 3rd ring road expressway in Beijing was used to build flow-density model and speed-density Greenshields model. Then the density was discussed based on the traffic wave speed function with cusp catastrophe theory; in particular, density conditions on median lanes and shoulder lanes were deeply discussed. Meanwhile the chaotic characteristics were analyzed based on the traffic temporal sequence data collected from 29 detectors located at the 3rd ring road expressway, and C-C method was used to reconstruct the phase space and the largest Lyapunov exponents were estimated by Wolf method and the small data sets method. The results indicated that the traffic operation catastrophe density on the median lanes was a bit higher than that on the shoulder lanes; additionally chaotic characteristics obviously existed in the local corridor composed of 29 detectors in the 3rd ring road expressway traffic flow system.
Jian Gu; Shuyan Chen. Nonlinear Analysis on Traffic Flow Based on Catastrophe and Chaos Theory. Discrete Dynamics in Nature and Society 2014, 2014, 1 -11.
AMA StyleJian Gu, Shuyan Chen. Nonlinear Analysis on Traffic Flow Based on Catastrophe and Chaos Theory. Discrete Dynamics in Nature and Society. 2014; 2014 ():1-11.
Chicago/Turabian StyleJian Gu; Shuyan Chen. 2014. "Nonlinear Analysis on Traffic Flow Based on Catastrophe and Chaos Theory." Discrete Dynamics in Nature and Society 2014, no. : 1-11.
This paper integrated superiority from time series model and least square support vector machine regression model with data aggregation for traffic speed short term forecasting. Based on the results of traffic data variations analysis, the practicability that speed data can be aggregated to several periods was confirmed, and aggregated model can be developed to forecast the speed with auto regression (AR) model and support vector machine regression (SVR). Then the speed data in case study were integrated to 4 periods at the location of Remote Traffic Microwave Sensors (RTMS) 2047 on 2ndRing Road Expressway in Beijing. Arguments with coefficients from AR models then act as the independent variables of LSSVR in aggregated model. Short term traffic speed was predicted by aggregated model, and the results indicated that taking advantages of time periods variation rule inside the aggregated model would help save the model running time cost under the premise of accuracy with better prediction ability than LSSVR in certain conditions.
Jian Gu; Shu Yan Chen. Traffic Speed Time Series Short Term Forecasting Using Aggregated Model. Applied Mechanics and Materials 2014, 587-589, 2057 -2062.
AMA StyleJian Gu, Shu Yan Chen. Traffic Speed Time Series Short Term Forecasting Using Aggregated Model. Applied Mechanics and Materials. 2014; 587-589 ():2057-2062.
Chicago/Turabian StyleJian Gu; Shu Yan Chen. 2014. "Traffic Speed Time Series Short Term Forecasting Using Aggregated Model." Applied Mechanics and Materials 587-589, no. : 2057-2062.
Predicting traffic incident duration is very important for advanced traffic incident management. An accurate prediction of incident duration contributes a lot to making appropriate decisions to deal with incidents for traffic managers and getting traffic information to travelers in a timely manner. Cyclic subspace regression (CSR) yields the least squares regression (LSR), principal component regression (PCR), partial least squares regression (PLSR), and a lot of middle regression methods during its solving process. According to a certain criterion, it can select optimal model parameters within a very broad solution space. In this paper, the cyclic subspace regression was applied to analyze the relationship between incident duration and its influence factors. The experiments' results indicated that the models based on cyclic subspace regression have a promising application to predict incident duration.
Xuanqiang Wang; Shuyan Chen; Jian Gu; Wenchang Zhen. Traffic Incident Duration Analysis Based on Cyclic Subspace Regression. ICTE 2013 2013, 2854 -2860.
AMA StyleXuanqiang Wang, Shuyan Chen, Jian Gu, Wenchang Zhen. Traffic Incident Duration Analysis Based on Cyclic Subspace Regression. ICTE 2013. 2013; ():2854-2860.
Chicago/Turabian StyleXuanqiang Wang; Shuyan Chen; Jian Gu; Wenchang Zhen. 2013. "Traffic Incident Duration Analysis Based on Cyclic Subspace Regression." ICTE 2013 , no. : 2854-2860.
A novel hybrid method based on wavelet transform and support vector regression was presented to forecast the short-term traffic speed. The method of multi-resolution was applied to analyse stochastic signal in the time series of short-term traffic speed. Considering the non-linear feature in the time series, we used different support vector machines, i.e., ε- support vector machine, ν-support vector machine and least square support vector machine, were applied respectively to the details and approximation from the wavelet transform to establish the hybrid forecasting model. The final prediction result of the hybrid model was obtained by integrating the forecasting results from the details and approximation. The proposed model was realized in MATLAB. Compared with some common SVR models, the performance and prediction results in our experiment demonstrated that the proposed method fitted better to the original data set obtained from real-world traffic speed data, meanwhile it had lower time cost and the predictive accuracy increased visibly.
Jian Gu; Shuyan Chen. Forecasting the Short-Term Traffic Flow Based on Wavelet Transform and Support Vector Regression. ICTIS 2013 2013, 1 .
AMA StyleJian Gu, Shuyan Chen. Forecasting the Short-Term Traffic Flow Based on Wavelet Transform and Support Vector Regression. ICTIS 2013. 2013; ():1.
Chicago/Turabian StyleJian Gu; Shuyan Chen. 2013. "Forecasting the Short-Term Traffic Flow Based on Wavelet Transform and Support Vector Regression." ICTIS 2013 , no. : 1.