Tema 3: la coordinación de la campaña en redes con la campaña general.
10. Propuesta de cronograma para la tesis doctoral
12.2 Modelo de entrevista
Section 2.3 discussed the main factors that may influence the development of prediction models for short-term traffic prediction. Section 2.4 reviewed the basic concepts and algorithms of statistical and machine learning methods in the application of short-term traffic variable prediction. Table 2.3 shows a comparison summarising the key features of literature reviewed in this chapter, under a number of headings covering the characteristics of the prediction context (urban vs freeway, nature and
Page | 81 temporal resolution of input data, prediction horizon), the characteristics of the prediction method used (parametric model based or non-parametric, nature of the training process and data requirements) and the nature of the traffic conditions (normal or abnormal) within which the models were implemented. Table 2.4, meanwhile, summarises the key characteristics, advantages and weaknesses of the existing prediction methods as reviewed in Section 2.4 (Hastie et al., 2001; Vlahogianni et al., 2004; Samoili & Dumont, 2012). In addition, Table 2.5 compares the statistical and machine learning methods reviewed in Section 2.4 in terms of data utilisation, prediction accuracy, model robustness, calibration, ease of implementation and transferability. There are three levels for each characteristic, namely Good, Fair and Poor.
Page | 82 Table 2.3: Categorisation of available literature in existing traffic prediction models
Author Context Input data resolution (min) Prediction step Input variables
Input data pattern Training
Traffic
Condition Method Structure Seasonal
temporal pattern
Spatial
pattern Process Dataset
Ahmed &
Cook (1979) Freeway 0.5 1 occupancy Flow and
Offline
(Calibration) Yes Normal ARIMA Parametric
Levin &
Tsao (1980) Freeway 20 1
Flow and occupancy
Offline
(Calibration) Yes Normal ARIMA Parametric
Hamed et al.
(1995) Urban road 1 1 Flow
Offline
(Calibration) Yes Normal ARIMA Parametric
Williams &
Hoel (2003) Freeway 15 1 Flow Yes
Offline
(Calibration) Yes Normal SARIMA Parametric
Ghosh et al. (2007)
Urban
road 15 1 Flow Yes
Offline
(Calibration) Yes Normal SARIMA Parametric
Guo et al.
(2012a) Urban road 15 1 Flow Yes Yes No No Normal & abnormal GM parametric Non-
Okutani & Stephanedes
(1984)
Urban
road 5 1 & 6 Flow Yes (Calibration) Offline Yes Normal KF Parametric
Stathopoulos & Karlaftis
(2003)
Urban
road 3 1 Flow Yes
Offline
(Calibration) Yes Normal KF Parametric
Park &
Rilett (1999) Freeway 5 1 & 5 Time Yes Yes
Offline
(Calibration) Yes Normal NN
Non- parametric
Page | 83 Table 2.3: Categorisation of available literature in existing traffic prediction models (Continued)
Author Context Input data resolution (min) Prediction step Input variables
Input data pattern Training
Traffic
Condition Method Structure Seasonal
temporal pattern
Spatial
pattern Process Dataset
Huang &
Ran (2002) Urban road 5 3 Flow, speed and weather Yes
Offline
(Calibration) Yes
Normal &
abnormal NN parametric Non- Park et al.
(1998) Freeway 5 1 Flow
Offline
(Calibration) Yes Normal NN
Non- parametric Abdulhai
et al.
(1999)
Freeway 0.5 1 & 30 Flow and
occupancy Yes Offline (Calibration) Yes NN Non- parametric Ishak & Alecsandru (2004) Freeway 5,
10,15,20 1, 2, 3 & 4 Speed Yes Yes
Offline
(Calibration) Yes NN
Non- parametric Zheng et
al. (2006) Freeway 15 1 Flow
Offline
(Calibration) Yes Normal NN
Non- parametric Davis & Nihan (1991) Freeway 1 1 Flow and occupancy Online
(Lazy) Yes Normal kNN
Non- parametric Smith &
Demetsky (1997)
Freeway 15 1 Flow Online
(Lazy) Yes Normal & abnormal kNN Non- parametric Smith et
al. (2002) Freeway 15 1 Flow Online (Lazy) Yes Normal kNN parametric Non-
Clark
(2003) Highway 10 1
Speed, flow &occupancy
Online
(Lazy) Yes Normal kNN
Non- parametric
Page | 84 Table 2.3: Categorisation of available literature in existing traffic prediction models (Continued)
Author Context Input data resolution (min) Prediction step Input variables
Input data pattern Training
Traffic
Condition Method Structure Seasonal
temporal pattern
Spatial
pattern Process Dataset
Turochy
(2006) Freeway 15 1
Speed, flow
&occupancy Yes Yes
Online
(Lazy) Yes Normal kNN
Non- parametric Krishnan & Polak (2008) Urban
road 15 1 & 4 Flow Yes Yes
Online
(Lazy) Yes Normal kNN
Non- parametric Tam & Lam (2009) Urban
road 5 1 Time Yes
Online
(Lazy) Yes Abnormal kNN
Non- parametric Guo et al.
(2010) Urban road 15 1 Flow Yes Yes Online (Lazy) Yes Normal & abnormal kNN parametric Non-
Guo et al. (2012b)
Urban
road 15 1 Flow Yes Yes
Online (Lazy) Yes Normal & abnormal kNN Non- parametric Sun et al.
(2003) Freeway 5 Yes Speed Yes
Online
(Lazy) Yes Normal KS
Non- parametric Huang &
Sadek
(2009) Freeway 5 1 Flow Yes
Online (Lazy) Yes Normal & abnormal SPN Non- parametric Wu et al. (2004) Highway 3 1 Time Offline
(Calibration) Yes Normal SVR
Non- parametric Castro-
Neto et al. (2009)
Freeway 5 1 Flow Yes (Calibration) Offline Yes Normal & abnormal SVR parametric Non-
(Leshem & Ritov, 2007) Urban road 30 1 Flow Offline
(Calibration) Yes Normal RF
Non- parametric
Page | 85 Table 2.4: Characteristics of reviewed statistical and machine learning methods in short-term traffic prediction
Methods Characteristics Advantages Weaknesses
Historical average
(e.g. Jeffery et al. (1987))
Use the historical average as the predictor
Values are pre-determined
Computationally efficient Simple structure
Inaccurate during abnormal conditions
ARIMA/SARIMA (e.g. Ahmed and Cook (1979); Levin & Tsao (1980); Hamed et al. (1995); Williams & Hoel (2003); Ghosh et al. (2007))
Statistic parametric method Linear or non-linear
Stochastic
Seasonal temporal structure
Simple structure Well-established theoretical background Computationally efficient Weak stationarity Weak transferability
Inaccurate prediction during abnormal traffic conditions
GM
(e.g. Guo et al. (2012a))
Non-linear
Successively updates
parameters with input feature vector
Easily detects the change of traffic pattern during abnormal traffic conditions
No training procedure
Better prediction performance than ARIMA
Requires high quality traffic data
Page | 86 Table 2.4: Characteristics of reviewed statistical and machine learning methods in short-term traffic prediction (Continued)
Methods Characteristics Advantages Weaknesses
KF
(e.g. Okutani and Stephanedes (1984); Stathopoulos & Karlaftis (2003))
Linear or non-linear
Stochastic Gaussian nature of initial conditions
Continuously updates parameters
Multivariate input Flexible model structure
Gaussian hypothesis Requires knowledge of
system's dynamics model System must be controllable
NN
(e.g. Park & Rilett (1999); Huang & Ran (2002); Park
et al. 1998; Abdulhai et al.
(1999); Van Lint (2004); Ishak & Alecsandru (2004); Zheng et al. (2006))
Non-linear Non-parametric
No requirements of hypothesis on the statistical nature of data
Multivariate model High prediction accuracy Acceptable prediction accuracy
during abnormal traffic conditions
Requires extensive training dataset
Complex selection of model parameters
SPN
(e.g. Huang & Sadek (2009))
Using historical average data Data merge and comparison
process
No training procedure Transferability
Page | 87 Table 2.4: Characteristics of reviewed statistical and machine learning methods in short-term traffic prediction (Continued)
Methods Characteristics Advantages Weaknesses
kNN
(e.g. Davis & Nihan (1991); Smith & Demetsky (1997); Smith et al. (2002); Clark (2003); Turochy (2006); Krishnan & Polak (2008); Tam & Lam (2009); Guo et al. (2010); Guo et al. (2012b)) Non-linear Non-parametric Pattern matching Model free Simple structure
High prediction accuracy Transferability
Robustness
Easy implementation
Acceptable prediction accuracy during abnormal traffic
conditions
Requires extensive historical dataset
KS
(e.g. Sun et al. (2003))
Similar to kNN method Simple structure Requires more computing time
than kNN SVR
(e.g. Wu et al. (2004); Castro- Neto et al. (2009))
Non-parametric
Map input feature vector into a high dimensional feature space
Transferability
Acceptable prediction accuracy during abnormal traffic
conditions
Requires extensive training dataset
RF Non-parametric
Based on the decision tree method
Simple structure
Acceptable prediction accuracy during abnormal traffic
conditions
Requires extensive training dataset
Page | 88 Table 2.5: Comparison of reviewed statistical/machine learning methods in traffic prediction
Method Historical data utilisation Real-time data utilisation Prediction accuracy Robustness Ease of implementation Computational efficiency Historical
average Good Poor Poor Poor Good Good
ARIMA Good Good Fair Poor Poor Fair
GM Fair Good Good Fair Good Good
KF Fair Good Good Poor Fair Fair
kNN Good Good Good Fair Good Fair
KS Good Good Fair Fair Good Fair
SVR Good Good Good Fair Fair Fair
NN Good Good Good Fair Fair Fair
SPN Good Good Fair Fair Poor Fair
Page | 89