16.7.2.5.6 Transit Traffic Analysis

Chapter Contents (Back)
Transit Usage.

Lin, W.S., Sheu, J.W.,
Metro Traffic Regulation by Adaptive Optimal Control,
ITS(12), No. 4, December 2011, pp. 1064-1073.
IEEE DOI 1112
BibRef

Li, L., Zhang, H., Wang, X., Lu, W., Mu, Z.,
Urban Transit Coordination Using an Artificial Transportation System,
ITS(12), No. 2, June 2011, pp. 374-383.
IEEE DOI 1101
BibRef

Noori, K., Jenab, K.,
Intelligent Traction Control Model for Speed Sensor Vehicles in Computer-Based Transit System,
ITS(13), No. 2, June 2012, pp. 680-690.
IEEE DOI 1206
BibRef

Blum, J.J., Mathew, T.V.,
Implications of the computational complexity of transit route network redesign for metaheuristic optimisation systems,
IET-ITS(6), No. 2, 2012, pp. 124-131.
DOI Link 1206
BibRef

Zhang, G., Zhang, H., Li, L., Dai, C.,
Agent-Based Simulation and Optimization of Urban Transit System,
ITS(15), No. 2, April 2014, pp. 589-596.
IEEE DOI 1404
Algorithm design and analysis BibRef

Wang, Y.H.[Yi-Hui], de Schutter, B., van den Boom, T.J.J., Ning, B.[Bin], Tang, T.[Tao],
Efficient Bilevel Approach for Urban Rail Transit Operation With Stop-Skipping,
ITS(15), No. 6, December 2014, pp. 2658-2670.
IEEE DOI 1412
integer programming BibRef

Daszczuk, W.B., Choromanīski, W., Miesīcicki, J., Grabski, W.,
Empty vehicles management as a method for reducing passenger waiting time in Personal Rapid Transit networks,
IET-ITS(9), No. 3, 2015, pp. 231-239.
DOI Link 1506
demand forecasting BibRef

Cadarso, L., Maroti, G., Marin, A.,
Smooth and Controlled Recovery Planning of Disruptions in Rapid Transit Networks,
ITS(16), No. 4, August 2015, pp. 2192-2202.
IEEE DOI 1508
Computational modeling BibRef

An, S.[Shi], Zhang, X.M.[Xin-Ming], Wang, J.[Jian],
Finding Causes of Irregular Headways Integrating Data Mining and AHP,
IJGI(4), No. 4, 2015, pp. 2604.
DOI Link 1601
Transit system flows. BibRef

Pinelli, F., Nair, R., Calabrese, F., Berlingerio, M., Di Lorenzo, G., Sbodio, M.L.,
Data-Driven Transit Network Design From Mobile Phone Trajectories,
ITS(17), No. 6, June 2016, pp. 1724-1733.
IEEE DOI 1606
Antennas BibRef

Zhao, J., Zhang, F., Tu, L., Xu, C., Shen, D., Tian, C., Li, X.Y., Li, Z.,
Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems,
ITS(18), No. 4, April 2017, pp. 790-801.
IEEE DOI 1704
Estimation BibRef

Duan, Z.Y.[Zheng-Yu], Lei, Z.X.[Zeng-Xiang], Zhang, M.[Michael], Li, W.F.[Wei-Feng], Fang, J.[Jia], Li, J.[Jian],
Understanding evacuation and impact of a metro collision on ridership using large-scale mobile phone data,
IET-ITS(11), No. 8, October 2017, pp. 511-520.
DOI Link 1710
BibRef

Zhao, J., Qu, Q., Zhang, F., Xu, C., Liu, S.,
Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data,
ITS(18), No. 11, November 2017, pp. 3135-3146.
IEEE DOI 1711
Companies, Data mining, Global Positioning System, Smart cards, Space exploration, Transportation, Urban areas, Passenger behavior analysis, metro system, smart card data, spatio-temporal, analysis BibRef

Zhou, Y.[Yang], Fang, Z.X.[Zhi-Xiang], Zhan, Q.M.[Qing-Ming], Huang, Y.P.[Ya-Ping], Fu, X.W.[Xiong-Wu],
Inferring Social Functions Available in the Metro Station Area from Passengers' Staying Activities in Smart Card Data,
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801
BibRef

Ni, M., He, Q., Gao, J.,
Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media,
ITS(18), No. 6, June 2017, pp. 1623-1632.
IEEE DOI 1706
Forecasting, Predictive models, Public transportation, Tagging, Twitter, Social media, event identification, social sensing, subway passenger flow prediction, transit, ridership BibRef

Singh, P.[Parul], Oh, K.[Kyuhyup], Jung, J.Y.[Jae-Yoon],
Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link 1710
BibRef

Dong, X., Lin, Y., Shen, D., Li, Z., Zhu, F., Hu, B., Fan, D., Xiong, G.,
A Parallel Transportation Management and Control System for Bus Rapid Transit Using the ACP Approach,
ITS(18), No. 9, September 2017, pp. 2569-2574.
IEEE DOI 1709
ACP approach, BRT adaptive operations, BRT forecasting, BRT incident management, BRT monitoring, BRT warning, Guangzhou BRT, PTMS-BRT, artificial systems, bus rapid transit, complex system theory, mass transit service improvement, parallel transportation management-and-control system, Roads, Scheduling, ACP approach, artificial transportation system, dynamic perception, BibRef

Zhu, F., Li, Z., Chen, S., Xiong, G.,
Parallel Transportation Management and Control System and Its Applications in Building Smart Cities,
ITS(17), No. 6, June 2016, pp. 1576-1585.
IEEE DOI 1606
Artificial intelligence BibRef

Xiong, G., Shen, D., Dong, X., Hu, B., Fan, D., Zhu, F.,
Parallel Transportation Management and Control System for Subways,
ITS(18), No. 7, July 2017, pp. 1974-1979.
IEEE DOI 1706
Accidents, Control systems, Generators, Monitoring, Planning, Public transportation, ACP approach, Subways, artificial subway system, computational experiments platform, parallel execution system, status, perception BibRef

Zhang, J., Shen, D., Tu, L., Zhang, F., Xu, C., Wang, Y., Tian, C., Li, X., Huang, B., Li, Z.,
A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems,
ITS(18), No. 11, November 2017, pp. 3168-3178.
IEEE DOI 1711
Estimation, Forecasting, Global Positioning System, Neural networks, Real-time systems, Smart cards, Transportation, BibRef

Ding, X.B.[Xiao-Bing], Liu, Z.G.[Zhi-Gang], Xu, H.B.[Hai-Bo],
The passenger flow status identification based on image and WiFi detection for urban rail transit stations,
JVCIR(58), 2019, pp. 119-129.
Elsevier DOI 1901
Rail transit, Safety of stations, Passenger flow identification, Emergency warning BibRef

Cong, J.M.[Jia-Min], Gao, L.J.[Lin-Jie], Juan, Z.C.[Zhi-Cai],
Improved algorithms for trip-chain estimation using massive student behaviour data from urban transit systems,
IET-ITS(13), No. 3, March 2019, pp. 435-442.
DOI Link 1903
BibRef

Jin, H.T.[Hai-Tao], Jin, F.J.[Feng-Jun], Zhu, H.[He],
Measuring Spatial Mismatch between Public Transit Services and Regular Riders: A Case Study of Beijing,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Duan, Z.Y.[Zheng-Yu], Lei, Z.X.[Zeng-Xiang], Zhang, M.[Michael], Li, H.F.[Hai-Feng], Yang, D.Y.[Dong-Yuan],
Understanding multiple days' metro travel demand at aggregate level,
IET-ITS(13), No. 5, May 2019, pp. 756-763.
DOI Link 1906
BibRef

Pei, M.Y.[Ming-Yang], Lin, P.Q.[Pei-Qun], Liu, R.H.[Rong-Hui], Ma, Y.Y.[Ying-Ying],
Flexible transit routing model considering passengers' willingness to pay,
IET-ITS(13), No. 5, May 2019, pp. 841-850.
DOI Link 1906
BibRef

Wang, W.[Weiyang], Hu, J.[Jia], Ji, Y.[Yuxiong], Du, Y.[Yuchuan],
Improving fuel efficiency of connected and automated transit buses on signallised corridors,
IET-ITS(13), No. 5, May 2019, pp. 870-879.
DOI Link 1906
BibRef

Ma, X., Zhang, J., Du, B., Ding, C., Sun, L.,
Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction,
ITS(20), No. 6, June 2019, pp. 2278-2288.
IEEE DOI 1906
Feature extraction, Predictive models, Data models, Spatiotemporal phenomena, Forecasting, Neural networks, parallel structure BibRef

Hou, Z., Dong, H., Gao, S., Nicholson, G., Chen, L., Roberts, C.,
Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow,
ITS(20), No. 7, July 2019, pp. 2774-2785.
IEEE DOI 1907
Delays, Energy consumption, Rail transportation, Software, Numerical models, Heuristic algorithms, Linear programming, ATO profile BibRef

Feng, J., Ye, Z., Wang, C., Xu, M., Labi, S.,
An Integrated Optimization Model for Energy Saving in Metro Operations,
ITS(20), No. 8, August 2019, pp. 3059-3069.
IEEE DOI 1908
Energy consumption, Acceleration, Optimization, Kinetic energy, Switches, Mathematical model, Genetic algorithms, Timetable, speed, cataclysmic genetic algorithm BibRef

Li, W., Cao, J., Guan, J., Zhou, S., Liang, G., So, W.K.Y., Szczecinski, M.,
A General Framework for Unmet Demand Prediction in On-Demand Transport Services,
ITS(20), No. 8, August 2019, pp. 2820-2830.
IEEE DOI 1908
Public transportation, Feature extraction, Predictive models, Vehicles, Electronic mail, Vehicle dynamics, Data mining, prediction model BibRef

Han, Y.[Yong], Wang, S.[Shukang], Ren, Y.[Yibin], Wang, C.[Cheng], Gao, P.[Peng], Chen, G.[Ge],
Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Guo, Z.Q.A.[Zhi-Qi-Ang], Zhao, X.[Xin], Chen, Y.[Yaxin], Wu, W.[Wei], Yang, J.[Jie],
Short-term passenger flow forecast of urban rail transit based on GPR and KRR,
IET-ITS(13), No. 9, September 2019, pp. 1374-1382.
DOI Link 1908
BibRef

Pang, J., Huang, J., Du, Y., Yu, H., Huang, Q., Yin, B.,
Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network,
ITS(20), No. 9, September 2019, pp. 3283-3293.
IEEE DOI 1909
Global Positioning System, Time measurement, Recurrent neural networks, Task analysis, Transportation, multi-step-ahead prediction BibRef

Koehler, L.A., Seman, L.O., Kraus, W., Camponogara, E.,
Real-Time Integrated Holding and Priority Control Strategy for Transit Systems,
ITS(20), No. 9, September 2019, pp. 3459-3469.
IEEE DOI 1909
Delays, Indexes, Real-time systems, Automobiles, Optimization, Predictive models, BRT, bus bunching, bus headway control, transit signal priority BibRef

Liu, H., Zhou, M., Guo, X., Zhang, Z., Ning, B., Tang, T.,
Timetable Optimization for Regenerative Energy Utilization in Subway Systems,
ITS(20), No. 9, September 2019, pp. 3247-3257.
IEEE DOI 1909
Optimization, Public transportation, Acceleration, Mathematical model, Resistors, Genetic algorithms, artificial bee colony BibRef

Qiu, G., Song, R., He, S., Xu, W., Jiang, M.,
Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus,
ITS(20), No. 9, September 2019, pp. 3351-3360.
IEEE DOI 1909
Clustering algorithms, Smart cards, Urban areas, Planning, Estimation, Prediction algorithms, density-based spatial clustering algorithm BibRef

Li, M.[Minmin], Guo, R.Z.[Ren-Zhong], Li, Y.[You], He, B.[Biao], Fan, Y.[Yong],
The Distribution Pattern of the Railway Network in China at the County Level,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Yang, D.[Dan], Chen, K.[Kairun], Yang, M.N.[Meng-Ning], Zhao, X.C.[Xiao-Chao],
Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features,
IET-ITS(13), No. 10, October 2019, pp. 1475-1482.
DOI Link 1909
BibRef

Han, Y.[Yong], Wang, C.[Cheng], Ren, Y.[Yibin], Wang, S.K.[Shu-Kang], Zheng, H.C.[Huang-Cheng], Chen, G.[Ge],
Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
BibRef


Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Traffic Signal Control, Traffic Analysis, Not Image Analysis .


Last update:Oct 1, 2019 at 15:23:24