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Operational Analysis of Signalized Street Segments Using Multi-gene Genetic Programming and Functional Network Techniques

  • Research Article - Civil Engineering
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Abstract

This article presents the operational analysis of urban signalized segments, the most fundamental entity of road networks, operating under heterogeneous traffic conditions. Geometric, traffic, and built-environmental data were collected from mid-block sections and downstream intersections of 45 well-diversified segments. Besides, the socio-demographic details, travel characteristics, and perceived satisfaction scores (varying from 1 \(=\) excellent to 6 \(=\) worst) were collected from 9000 on-street automobile drivers. Subsequently, the variables having significant (\(p < 0.001\)) influences on the perceived satisfaction scores were identified by Spearman’s correlation analysis. As observed, the array of significant variables included six quantitative road attributes and the age group of motorists. By incorporating these variables, highly reliable but less complex automobile level of service (ALOS) models were developed with the help of two novel artificial intelligence techniques namely, multi-gene genetic programming (MGGP) and functional network (FN). Both models exhibited excellent prediction efficiencies in the present context and produced high coefficient of determination (\(R^{2}\)) values of above 0.86 under the prevailing site conditions. The model comparison showed that the MGGP model is more reliable and easier for field implementations as compared to the FN model. The sensitivity analyses of modeled attributes revealed that traffic volume, travel speed, and automobile stop rate have by far the most significant influences on the ALOS of urban streets. The crucial outcomes of this study would largely help the transportation planners and engineers in quantifying the operational efficiencies of urban roadways and in taking efficient decisions for the better management of automobile traffic.

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Abbreviations

AAE:

Average absolute error

AI:

Artificial intelligence

ALOS:

Automobile level of service

ANN:

Artificial neural network

AP:

Affinity propagation

CAF:

Coordination adjustment factor

CBD:

Central business district

CCF:

Cyclic coordination function

FCM:

Fuzzy c-means

FFS:

Free flow speed

FN:

Functional networks

GA:

Genetic algorithm

GP:

Genetic programming

GPS:

Global positioning system

Hardcl:

Hard competitive learning

HCM:

Highway Capacity Manual

IRC:

Indian Road Congress

LOS:

Level of service

MAPE:

Mean absolute percentage error

MGGP:

Multi-gene genetic programming

OR:

Overfitting ratio

QOP:

Quality of progression

RMSE:

Root-mean-square error

SF:

Shape function

SSE:

Sum of squared error

AG :

Age group

\(a_{i}\) :

Coefficient of \(\phi \)(x) at ith degree

\({\textit{ALOS}}_{{\mathrm{Max}}}\) :

Maximum value of overall perceived ALOS score

\({\textit{ALOS}}_{{\mathrm{Min}}}\) :

Minimum value of overall perceived ALOS score

\({\textit{ALOS}}_{{\mathrm{Norm}}}\) :

Normalized value of overall perceived ALOS score

\({\textit{ALOS}}_{{\mathrm{Overall}}}\) :

Overall perceived ALOS score

\(\alpha _{k}\), \(x_{0}\) :

Constant terms

C :

Cycle length

D :

Maximum deviation in the model output

\(D_{{\mathrm{Avg}}}\) :

Average delay

\(D_{{\mathrm{Freq}}}\) :

Frequency of driveways carrying high traffic volume

\(d_{{\mathrm{max}}}\) :

Maximum depth of GP tree

E :

Nash–Sutcliffe efficiency coefficient

\(e_{j}\) :

Error in the jth data

\(E_{n}\) :

Sum of squared errors for n data sets

\(E_{\lambda }\) :

An auxiliary function

\(f'\) :

Functional element

F :

Model function

f, g :

Any function

\(f_{{\mathrm{max}}}(x_{i})\) :

Maximum value of the predicted output over ith input

\(f_{{\mathrm{min}}}(x_{i})\) :

Minimum value of the predicted output over ith input

g :

Green period

g / C :

Green time over cycle length

\(G_{{\mathrm{max}}}\) :

Maximum number of genes

L :

Number of lanes

LU :

Roadside land use pattern

m :

Degree of shape functions

n :

Number of genes in the target expression

p :

Significance level

PHV :

Peak hour volume

\(R^{2}\) :

Coefficient of determination

S :

Average traffic speed

s :

Number of independent variables

\(S_{i}\) :

Percentage contribution ith input

SR :

Spatial stop rate

v :

Variable

\(w_{0}\) :

Bias parameter

\(w_{i}\) :

Weight of ith gene

x :

Input unit or output unit

X :

Vector of influencing variables

\(x_{1}, x_{2},{\ldots }, x_{\mathrm{s}}\) :

FN model inputs

\(x_{{\mathrm{s}}+1}\) :

FN model output

y :

MGGP model output

\(\rho \) :

Rho

\(\phi _{i}\) :

Shape function of an input variable at ith degree

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Rourkela, Odisha, 769008, India

    Sambit Kumar Beura & Prasanta Kumar Bhuyan

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  1. Sambit Kumar Beura
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Correspondence to Sambit Kumar Beura.

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Beura, S.K., Bhuyan, P.K. Operational Analysis of Signalized Street Segments Using Multi-gene Genetic Programming and Functional Network Techniques. Arab J Sci Eng 43, 5365–5386 (2018). https://doi.org/10.1007/s13369-018-3176-4

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