16.7.2.5.8 Transit Traffic Analysis, Public Transit, Bus

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, F., Jin, B., Wang, Z., Liu, H., Hu, J., Zhang, L.,
On Geocasting over Urban Bus-Based Networks by Mining Trajectories,
ITS(17), No. 6, June 2016, pp. 1734-1747.
IEEE DOI 1606
Delays BibRef

Jung, J.[Jaeyoung], Sohn, K.[Keemin],
Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data,
IET-ITS(11), No. 6, August 2017, pp. 334-339.
DOI Link 1707
BibRef

Chen, C.[Chao], Zhang, D.[Daqing], Li, N.[Nan], Zhou, Z.H.[Zhi-Hua],
B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces,
ITS(15), No. 4, August 2014, pp. 1451-1465.
IEEE DOI 1410
Global Positioning System 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

van der Hurk, E., Kroon, L., Maroti, G., Vervest, P.,
Deduction of Passengers' Route Choices From Smart Card Data,
ITS(16), No. 1, February 2015, pp. 430-440.
IEEE DOI 1502
Clocks BibRef

Lin, Y., Wan, H., Jiang, R., Wu, Z., Jia, X.,
Inferring the Travel Purposes of Passenger Groups for Better Understanding of Passengers,
ITS(16), No. 1, February 2015, pp. 235-243.
IEEE DOI 1502
Business BibRef

Syrjarinne, P., Nummenmaa, J., Thanisch, P., Kerminen, R., Hakulinen, E.,
Analysing traffic fluency from bus data,
IET-ITS(9), No. 6, 2015, pp. 566-572.
DOI Link 1509
data mining BibRef

Hosu, A.C., Kiss, Z.I., Ivanciu, I.A., Varga, M., Polgar, Z.A.,
Integrated ubiquitous connectivity and centralised information platform for intelligent public transportation systems,
IET-ITS(9), No. 6, 2015, pp. 573-581.
DOI Link 1509
public transport 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

Nunes, A.A., Galvao Dias, T., Falcao e Cunha, J.,
Passenger Journey Destination Estimation From Automated Fare Collection System Data Using Spatial Validation,
ITS(17), No. 1, January 2016, pp. 133-142.
IEEE DOI 1601
Accuracy 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

Liu, Z., Jiang, S., Zhou, P., Li, M.,
A Participatory Urban Traffic Monitoring System: The Power of Bus Riders,
ITS(18), No. 10, October 2017, pp. 2851-2864.
IEEE DOI 1710
Global Positioning System, Mobile handsets, Monitoring, Probes, Roads, Sensors, Urban areas, Urban traffic monitoring, bus riders, bus systems, cellular signal, participatory, sensing 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

Huang, J.W.[Jian-Wei], Liu, X.T.[Xin-Tao], Zhao, P.X.[Peng-Xiang], Zhang, J.W.[Jun-Wei], Kwan, M.P.[Mei-Po],
Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Zhao, J., Zhou, X.,
Improving the Operational Efficiency of Buses With Dynamic Use of Exclusive Bus Lane at Isolated Intersections,
ITS(20), No. 2, February 2019, pp. 642-653.
IEEE DOI 1902
Delays, Optimization, Vehicle dynamics, Roads, Resource management, Legged locomotion, Exclusive bus lane, dynamic control, signalized intersections BibRef

Pili, F.[Francesco], Olivo, A.[Alessandro], Barabino, B.[Benedetto],
Evaluating alternative methods to estimate bus running times by archived automatic vehicle location data,
IET-ITS(13), No. 3, March 2019, pp. 523-530.
DOI Link 1903
BibRef

He, P., Jiang, G., Lam, S., Tang, D.,
Travel-Time Prediction of Bus Journey With Multiple Bus Trips,
ITS(20), No. 11, November 2019, pp. 4192-4205.
IEEE DOI 1911
Trajectory, Public transportation, Predictive models, Real-time systems, Prediction algorithms, Data models, interval-based historical average BibRef

Jia, F.F.[Fei-Fan], Li, H.Y.[Hai-Ying], Jiang, X.[Xi], Xu, X.Y.[Xin-Yue],
Deep learning-based hybrid model for short-term subway passenger flow prediction using automatic fare collection data,
IET-ITS(13), No. 11, November 2019, pp. 1708-1716.
DOI Link 1911
BibRef

Moyo, T., Musakwa, W.,
Exploring The Potential of Crowd Sourced Data to Map Commuter Points Of Interest: a Case Study of Johannesburg,
C3MGBD19(1587-1592).
DOI Link 1912
BibRef

Yang, X.P.[Xi-Ping], Lu, S.[Shiwei], Zhao, W.F.[Wei-Feng], Zhao, Z.Y.[Zhi-Yuan],
Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China,
IJGI(8), No. 11, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Cui, Z.Y.[Zhi-Yong], Long, Y.[Ying],
Perspectives on stability and mobility of transit passenger's travel behaviour through smart card data,
IET-ITS(13), No. 12, December 2019, pp. 1761-1769.
DOI Link 1912
BibRef

Zhou, M., Dong, H., Zhao, Y., Ioannou, P.A., Wang, F.,
Optimization of Crowd Evacuation With Leaders in Urban Rail Transit Stations,
ITS(20), No. 12, December 2019, pp. 4476-4487.
IEEE DOI 2001
Force, Dynamics, Heuristic algorithms, Optimization, Rails, Public transportation, Optimization, evacuation, leader, extended maximal covering location method BibRef

Egan, M., Oren, N., Jakob, M.,
Hybrid Mechanisms for On-Demand Transport,
ITS(20), No. 12, December 2019, pp. 4500-4512.
IEEE DOI 2001
Pricing, Public transportation, Vehicles, Routing, Probability density function, Urban areas, Resource management, pricing BibRef

Chen, X.[Xi], Wang, Y.[Yinhai], Tang, J.[Jinjun], Dai, Z.[Zhuang], Ma, X.L.[Xiao-Lei],
Examining regional mobility patterns of public transit and automobile users based on the smart card and mobile Internet data: a case study of Chengdu, China,
IET-ITS(14), No. 1, January 2020, pp. 45-55.
DOI Link 2001
BibRef

Gokasar, I., Cetinel, Y., Baydogan, M.G.,
Estimation of Influence Distance of Bus Stops Using Bus GPS Data and Bus Stop Properties,
ITS(20), No. 12, December 2019, pp. 4635-4642.
IEEE DOI 2001
Vegetation, Global Positioning System, Public transportation, Linear regression, Regression tree analysis, Data mining, public transport BibRef

Barabino, B., Coni, M., Olivo, A., Pungillo, G., Rassu, N.,
Standing Passenger Comfort: A New Scale for Evaluating the Real-Time Driving Style of Bus Transit Services,
ITS(20), No. 12, December 2019, pp. 4665-4678.
IEEE DOI 2001
Kinematics, Parameter estimation, Intelligent vehicles, Accelerometers, On-board bus comfort, passengers' perceptions BibRef

Zhang, Y., Cheng, T.,
A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data,
ITS(21), No. 2, February 2020, pp. 617-629.
IEEE DOI 2002
Employment, Feature extraction, Deep learning, Predictive models, Smart cards, Transportation, Correlation, Deep learning, temporal travel behavior BibRef

Markou, I., Rodrigues, F., Pereira, F.C.,
Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Information,
ITS(21), No. 2, February 2020, pp. 641-652.
IEEE DOI 2002
Public transportation, Predictive models, Urban areas, Deep learning, Internet, Semantics, Time series forecasting, deep Gaussian processes BibRef

Falsafain, H., Tamannaei, M.,
A Novel Dynamic Programming Approach to the Train Marshalling Problem,
ITS(21), No. 2, February 2020, pp. 701-710.
IEEE DOI 2002
Heuristic algorithms, Optimization, Complexity theory, Dynamic programming, Approximation algorithms, Search problems, train marshalling problem BibRef

Islam, M.F.[Md Faqhrul], Fonzone, A.[Achille], MacIver, A.[Andrew], Dickinson, K.[Keith],
Use of ubiquitous real-time bus passenger information,
IET-ITS(14), No. 3, March 2020, pp. 139-147.
DOI Link 2003
BibRef

Hu, R.[Rong], Chiu, Y.C.[Yi-Chang], Hsieh, C.W.[Chih-Wei],
Crowding prediction on mass rapid transit systems using a weighted bidirectional recurrent neural network,
IET-ITS(14), No. 3, March 2020, pp. 196-203.
DOI Link 2003
BibRef

Jiao, J.F.[Jun-Feng], Cai, M.M.[Ming-Ming],
Using Open Source Data to Identify Transit Deserts in Four Major Chinese Cities,
IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Du, B., Peng, H., Wang, S., Bhuiyan, M.Z.A., Wang, L., Gong, Q., Liu, L., Li, J.,
Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction,
ITS(21), No. 3, March 2020, pp. 972-985.
IEEE DOI 2003
Predictive models, Deep learning, Market research, Data models, Convolution, Public transportation, urban computing BibRef

Achar, A., Bharathi, D., Kumar, B.A., Vanajakshi, L.,
Bus Arrival Time Prediction: A Spatial Kalman Filter Approach,
ITS(21), No. 3, March 2020, pp. 1298-1307.
IEEE DOI 2003
Travel time prediction, Kalman filter, time series, non-stationary BibRef

Chen, E., Ye, Z., Wang, C., Xu, M.,
Subway Passenger Flow Prediction for Special Events Using Smart Card Data,
ITS(21), No. 3, March 2020, pp. 1109-1120.
IEEE DOI 2003
Public transportation, Data models, Predictive models, Analytical models, Autoregressive processes, Smart cards, Asymmetry BibRef

Laskaris, G., Seredynski, M., Viti, F.,
Enhancing Bus Holding Control Using Cooperative ITS,
ITS(21), No. 4, April 2020, pp. 1767-1778.
IEEE DOI 2004
Public transport, holding strategy, cooperative ITS, driver advisory systems BibRef

Zhang, K., Liu, Z., Zheng, L.,
Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning,
ITS(21), No. 4, April 2020, pp. 1480-1490.
IEEE DOI 2004
Short-term passenger demand prediction, multi-task learning, deep learning, convolutional neural network BibRef

Seo, J.[Jeongwook], Cho, S.H.[Shin-Hyung], Kim, D.K.[Dong-Kyu], Park, P.Y.J.[Peter Young-Jin],
Analysis of overlapping origin-destination pairs between bus stations to enhance the efficiency of bus operations,
IET-ITS(14), No. 6, June 2020, pp. 545-553.
DOI Link 2005
BibRef

Jenelius, E.,
Data-Driven Metro Train Crowding Prediction Based on Real-Time Load Data,
ITS(21), No. 6, June 2020, pp. 2254-2265.
IEEE DOI 2006
Real-time systems, Automobiles, Load modeling, Predictive models, Focusing, Regression tree analysis, Public transit, metro, crowding, boosted tree ensemble BibRef

Liu, R., Li, S., Yang, L., Yin, J.,
Energy-Efficient Subway Train Scheduling Design With Time-Dependent Demand Based on an Approximate Dynamic Programming Approach,
SMCS(50), No. 7, July 2020, pp. 2475-2490.
IEEE DOI 2006
Public transportation, Energy consumption, Optimization, Computational modeling, Heuristic algorithms, train scheduling BibRef

Daszczuk, W.B.,
Measures of Structure and Operation of Automated Transit Networks,
ITS(21), No. 7, July 2020, pp. 2966-2979.
IEEE DOI 2007
Benchmark testing, Entropy, Throughput, Analytical models, Intelligent transportation systems, Size measurement, systems engineering and theory-modeling-simulation-systems simulation BibRef

Xia, F., Wang, J., Kong, X., Zhang, D., Wang, Z.,
Ranking Station Importance With Human Mobility Patterns Using Subway Network Datasets,
ITS(21), No. 7, July 2020, pp. 2840-2852.
IEEE DOI 2007
Public transportation, Planning, Complex networks, Analytical models, Urban areas, Human mobility patterns, subway networks BibRef


Moyo, T., Musakwa, W.,
Ranking Nodes in Complex Networks: a Case Study of The Gaubus,
SmartGeoApps19(1333-1338).
DOI Link 1912
Routing. BibRef

Jamalul Shamsudin, N.L., Abdul Khanan, M.F., Umar, H.A., Atan, S.N., Din, A.H.M.,
Integrating Network Concept Into Multi Criteria Analysis for Suggesting Bus Rapid Transit Routes,
GGT19(309-317).
DOI Link 1912
BibRef

Vergara, K.A., Sanchez, J., Bautista, E.L.,
Site Selection for New Point to Point (P2P) Bus Endpoints and Routes In Metro Manila, Philippines,
GGT19(659-666).
DOI Link 1912
BibRef

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


Last update:Jul 10, 2020 at 16:03:35