time series analysis brt delhi
TRANSCRIPT
-
8/13/2019 Time Series Analysis Brt Delhi
1/81
TIME SERIES ANALYSIS OF VEHICULAR
DELAY IN DELHI
by
MEHVESH MUSHTAQEntry No. 2010CEP3291
Submitted
In partial fulfillment of the requirements for the award of the degree of
MASTER OF TECHNOLOGY
In
TRANSPORTATION ENGINEERING
Under the supervision of
Prof. Geetam Tiwari
Dr.A.K.Swamy
Department of Civil EngineeringIndian Institute of Technology Delhi
August, 2013
-
8/13/2019 Time Series Analysis Brt Delhi
2/81
ACKNOWLEDGEMENTS
I take this opportunity to express my regards, indebtedness and profound sense of
gratitude to my supervisor Prof. Geetam Tiwari and Dr.A.K.Swamy for their
inspiring guidance, constant encouragement and ever cooperating attitude, which
enable me to undertake the present work. I appreciate their understanding, untiring
enthusiasm and the great care they took in bringing up the work in the present form.
My foremost thanks are due to my parents for their encouragement, support, love
and affection and moral boosting, which kept me going throughout the duration of
the work.
I sincerely thank Dr.Manika Agarwal(DIMTS) for providing the data used in this
project and TRIPP (Transportation Research and Injury Prevention Programme),
especially Mr.Rahul Goel, Research Scholar, TRIPP, for providing all the necessary
data and help regarding the work.
August, 2013
Mehvesh Mushtaq
(2010CEP3291)
-
8/13/2019 Time Series Analysis Brt Delhi
3/81
CERTIFICATE
This is to certify that the thesis title Time Series Analysis of Vehicular Delay in
Delhi is a bonafide record of work done by Mehvesh Mushtaq for partial
fulfillment of the requirement for the degree of Master of Technology in
Transportation Engineering, Department of Civil Engineering, Indian Institute of
Technology (IIT) Delhi, New Delhi, India. She has fulfilled the requirements for the
submission of this thesis, which to the best of my knowledge has reached the
required standard.
This thesis was carried out under my supervision and guidance and has not been
submitted elsewhere for the award of any other degree.
(Dr. Geetam Tiwari)
Professor
Department of Civil Engineering,
Indian Institute of Technology Delhi,
New Delhi, India
Dr.A.K.Swamy
Assistant Professor
Department of Civil Engineering,
Indian Institute of Technology Delhi,
New Delhi, India
-
8/13/2019 Time Series Analysis Brt Delhi
4/81
ABSTRACT
Speed Studies can be temporal or spatiali.e., studying speed variations over time or
over space respectively. The objective of this project is to study temporal variation
of Bus speed over various bus routes of Delhi and identify bottlenecks in traffic in
time and space. It also aims to divide a route into segments, each segment being a
part of road between Stopping points like Bus Stops, Intersections(3 ways,4 ways),
and Roundabouts. The mean speed over each segment is calculated for all hourly
time slots during which buses ply on the route for all days of the week. Thus an
hourly speed profile for all sections of the route is available for all times of busmovement.
-
8/13/2019 Time Series Analysis Brt Delhi
5/81
TABLE OF CONTENTS:Chapter1.
1 Introduction
1.1 Definition of Time Series... .7
1.2 Autocorrelation... 8
1.3 Correlograms... 8
1.4 Box-Jenkins Models (Forecasting). 8
1.5 Why Time Series. 8
Chapter2.
2 Literature Review
2.1 Purpose of Literature review.10
2.2 Literature review...10
Chapter 3.
3 Data Collection and Analysis
3.1 Description of Available Data...23
3.2 Treatment of dataset123
3.3 Stationarity..24
3.4 Methodology of Project (for dataset1)..25
3.5 Results of Application of ADF test on data..26
3.6 Interpretations of Results. .27
3.7 Data Set 2..28
3.7.1 Route 108 Up.31
3.7.2 Route 108 Down...36
3.7.3 Route 185 Up....41
3.7.4 Route 185 Down.....49
3.7.5 Route 411 Up....54
3.7.6 Route 411 Down... 59
Chapter 4.
4. Conclusions .....69
4.1 Definition of Bottleneck...70
4.2 Scope for further studies...72
References....80
-
8/13/2019 Time Series Analysis Brt Delhi
6/81
LIST OF FIGURES:
Figure 1: Route 108 Up.31
Figure 2: Route 108 Down.36
Figure 3: Route 185 Up.41
Figure 4: Route 185 Down49
Figure 5: Route 411 Up.54
Figure 6: Route 411 Down59
Figure 7: Comparison chart of mean speed for all routes.....69
Figure 8: Comparison of mean speeds for different time slots.71
-
8/13/2019 Time Series Analysis Brt Delhi
7/81
LIST OF TABLES:
Table1. Critical values for DF and ADF tests...26
Table2. Compiled Results of ADF test on Dataset 1....27
Table3. Route Characteristics for Route 10830
Table4. Segments for Route analysis 108 Up...32
Table5.Conclusions for Route 108 Up..34
Table6. Segments for Route 108 Down36
Table7.Conclusions for Route 108 Down38
Table8. Route Characteristics for Route 18539
Table9.Segments for Route 185 Up..42
Table10.Conclusions for Route 185 Up.44
Table11. Segments for Route 185 Down..49
Table12.Conclusions for Route 185 Down...51
Table13. Segments for Route 411 Up...55
Table14.Conclusions for Route 411 Up56
Table15. Segments for Route 411 Down..60
Table16.Conclusions for Route 411 Down...62
Table17.Comparison of mean speed for different time slots.......70
Table 18. Bottleneck speed...71
Table 19. Comparison chart of Mean speeds over various Routes...73
-
8/13/2019 Time Series Analysis Brt Delhi
8/81
CHAPTER - 1
INTRODUCTION
Speed Studies can be temporal or spatial i.e., studying speed variations over time
and over space respectively. The objective of this project is to study temporal
variation of Bus speed over various bus routes of Delhi and identify bottlenecks in
traffic in time and space. It also aims to divide a route into segments, each segment
being a part of road between Stopping points like Bus Stops, Intersections (Three
ways,Four ways), and Roundabouts. The mean speed over each segment is
calculated for all hourly time slots during which buses ply on the route for all days of
the week. Thus an hourly speed profile for all sections of the route is available for all
times of bus movement.
The data used for this study is GPS (Global Positioning System) Data which
provides the location of a Particular bus after regular intervals of time (in the data
used for this project it is 10 secs approx.).This is ideal for studying the data as a time
series as a Time-series is essentially an ordered sequence of values of a variable atequally spaced time intervals.
Introduction to Time-Series
1.1. Definition of Time Series: An ordered sequence of values of a variable at
equally spaced time intervals. Time series analysis accounts for the fact that data
points taken over time may have an internal structure (such as autocorrelation, trend
or seasonal variation) that should be accounted for.
1.1.1. Applications:The usage of time series models is twofold:
a) Obtain an understanding of the underlying forces and structure that produced
the observed data.
b) Fit a model and proceed to forecasting, monitoring or even feedback and feed
forward control.
-
8/13/2019 Time Series Analysis Brt Delhi
9/81
1.1.2. Types of time series:
I) Continuous vs. Discrete:
Continuousobservations made continuously in time;
Discreteobservations made only at certain times.
II) Stationary vs. Non-stationary:
StationaryData that fluctuate around a constant value;
Non-stationary A series having parameters of the cycle (i.e., length,
amplitude or phase) change over time.
III) Deterministic vs. Stochastic:
Deterministic time seriesThis data can be predicted exactly;
Stochastic time series Data are only partly determined by past values and future
values have to be described with a probability distribution. This is the case for most,
if not all, natural time series. So many factors involved in a natural system that we
cannot possibly correctly apply all of them.
1.2 Autocorrelation: A series of data may have observations that are not
independent of one another. To find out if autocorrelation exist, Autocorrelation
Coefficients measure correlations between observations a certain distance apart.
1.3 Correlograms: The autocorrelation coefficient r(k) can then be plotted against
the lag (k) to develop a correlogram. This will give us a visual look at a range of
correlation coefficients at relevant time lags so that significant values may be seen.
1.4 Box-Jenkins Models (Forecasting): Box and Jenkins developed theAutoRegressive Integrative Moving Average (ARIMA) model which combined the
AutoRegressive (AR) and Moving Average (MA) models developed earlier with a
differencing factor that removes in trend in the data.
1.5 Why Time Series?
What we need from a modeling technique or a data-analysis tool is an ability to
respond quickly, provide simple forecasting techniques and ability to provide
accurate detailed local forecasts.
-
8/13/2019 Time Series Analysis Brt Delhi
10/81
Limitations of traditional Complex Model Systems and Model Packages:
a) Data collection and preparation is an enormous task (because of behavioraland socio-economic variables).
b) Results are less accurate than trend extrapolation or experts judgmentc) Forecasting errors:
As much as 90% for a 7-year forecast
Average error for 7 yr forecast30%
Average error for 3 yr forecast20% (Horowitz and Enslie, 1978)
d) Techniques for short-range planning are simpler but still inaccurate.
Also, The response of the most popular of these techniques (decomposition,
exponential smoothing, moving average),to significant traffic changes is inadequate,
hence they cannot predict traffic volume or other such variables with accuracy
(Holmesland (1979)).
Time Series analysis:
Time series has recently become a more attractive tool for traffic engineers.
Traditionally, traffic engineers do not explicitly assume that successive events are
correlated and usually consider events in the time domain to vary randomly around a
trend line. Autocorrelation was also ignored because the calculation and adjustment
required for it, was tedious. However, recently developed computer software makes
this a much easier and very inexpensive process.
In summary, time-series analysis is an attractive tool for analysis because:
a) we have exhausted most of the possibilities within the old set of forecastingtechniques,
b) many of these existing techniques are not giving us good solutions,c) we have the tools to extend our work into consideration of autocorrelated
events.
-
8/13/2019 Time Series Analysis Brt Delhi
11/81
CHAPTER - 2
LITERATURE REVIEW
2.1. Purpose of Literature Review:
The aim of the literature review is to summarize the major work done in the study of
travel time variation. It includes study of travel time variation, modeling of travel
time, travel time prediction.
2.2. Literature ReviewPaper no.1
Title: Analysis of travel time variation over multiple sections of Hanshin
Expressway in Japan
The paper classifies sources of uncertainty in travel time into the categories:
demand-side factors ( like traffic volume), supply-side factors (like traffic accidents)
and external effects (like rainfall intensity).It also classifies seven sources of events
that cause travel time variation: Traffic-influence events( traffic incidents, work
zones, weather),traffic demand events(fluctuations in normal traffic, special
events),physical highway features(traffic control devices and bottlenecks).The
Seemingly Unrelated Regression Equations (SURE) model was used by the authors,
as opposed to traditional models like Multiple Linear Regression (MLR) model as
the latter fail to estimate the error correlation across various equations, also called
contemporaneous error correlation. The papers novelty is also in that it considers
the effect of uncertainties on travel-time variation across multiple sections.
Methodology: The study assumes a linear relationship between the travel-time and
the factors affecting the travel-time variation. The route is divided into 3 sections
based on on-ramp and off-ramp criteria. The sections are considered dependent and
hence, the error covariance across the equation is not zero. Since it is believed that
there could be several unobserved characteristics of the uncertainties among various
sections that will affect the travel-time variation, therefore the error terms can be
-
8/13/2019 Time Series Analysis Brt Delhi
12/81
correlated across sections. Therefore, the regression equations are estimated jointly
as a set of Seemingly Unrelated Regression Equations.
Travel-time estimation for the study area: Using the collected vehicle detector data,
spot speed for every 500mt interval was estimated. Corresponding travel-times were
estimated from these. Path travel time for the three sections were estimated using
time-slice method, which was found to be more suitable for offline application rather
than online application when speed varies over time. Time-slice method was also
found to provide better results than the instantaneous method.
Travel time statistical parameters like mean, median, standard deviation, probability,
cumulative distribution and standard deviation were calculated.MLR analysis was
carried out to understand the influence of all the incidents on travel-time variation.
The residual error obtained by this model was used for estimating the error
covariance matrix. Using the error covariance matrix, SURE model coefficients were
estimated.
Conclusions:
The Standard Error (SE) obtained using the SURE model for the three sections was
lower than the MLR model. The model coefficients obtained by this method werefound to be more appropriate than those obtained from the MLR model. The
coefficients estimated by the MLR model underestimate the travel time as compared
to the SURE model. Except for free-flow situations, the results obtained by the
independent models have over-estimated the travel time under the influence of
correlation among various sections due to traffic-volume (demand-side factor),
traffic-accident (supply-side factor) and rainfall (external factor).
Paper no: 2
Title: Bus Arrival Time Prediction Using Artificial Neural Network Model.
Aim: The aim of this work was to develop and apply a model to predict bus arrival
time using AVL (Automatic Vehicle Location) data. The data considered are traffic
congestion and dwell time data.
Methodology: A historical data based model, regression models and an artificial
neural network model were used. The difference between the predicted and observedarrival times was used to qualify accuracy.
-
8/13/2019 Time Series Analysis Brt Delhi
13/81
AVL data was collected in Houston, Texas over 6 months in 2000(from June to
November) by Houston Metro buses equipped with DGPS (Differential Global
Positioning System) receiver at 5 second interval. The test bed was Route 60 which
was highly congested in the morning and afternoon peaks; it had two corridors, a
downtown and a north area corridor, and only the south-bound direction was studied.
The input variables were arrival time, dwell time, and schedule adherence. The time
periods were weekday peak, weekday nonpeak, weekday evening, weekend. It was
found that the variability of dwell time is larger than that of arrival time.
Models:
Historical Data Based Model: Link travel time between transit stops is calculated. It
includes stopped delay at intersections but does not include dwell times. Arrival
times are calculated at transit stops.
Regression Models: Five multiple linear regression specifications were tested in this
research after analyzing stepwise regression and correlation coefficient. Dwell time
was not used to develop regression models since it was not important statistically.
Artificial Neural Network Models: ANNs emulate the learning process of the
human brain. They are calibrated in two steps: training, and testing. Out of 13different training functions, Levernberg-Marquardt optimization algorithm was
chosen as the training function.
The ANN architecture used had three layers: an input layer, a hidden layer, an output
layer. The weights and parameters associated with the hidden layer were identified
during the calibration process. Fifteen different hidden neurons were tested and the
best number of neurons was selected for each ANN model based on the concept of
minimizing the prediction error. The prediction results from the fifteen different
neurons were not significantly different from each other. The back propagation
algorithm and the Hyperbolic Tangent Sigmoid transfer function were used in the
model development.
After testing fourteen different learning functions, a Perceptron Weight and Bias
learning function was used. The average MAPE (mean absolute percentage error) for
these fourteen functions was not significantly different.
-
8/13/2019 Time Series Analysis Brt Delhi
14/81
Model Evaluation: The MAPE was used as a MOE (measure of effectiveness) in this
work. It was found that clustering data led to smaller MAPE in Historic data based
model and regression models. However, the clustering results in poorer results than
the non-clustering option in the artificial neural network models. It is, therefore,
hypothesized that ANN as a universal function approximator, was able to identify
the non-linear relationships associated with different clusters. However, there may
not have been enough observations to adequately fit the functions.
The lowest MAPE of the Historical model of downtown area was for the weekday
peak. It is proposed that congestion reduces the variability in travel times and this
makes the historical model more accurate for this time period. The use of Real-time
schedule adherence did not improve the results much and hence, it was proposed that
there is a non-linear relationship between arrival time and schedule adherence. The
ANN has the lowest MAPE as compared to the Historic model and the MLR
(Multiple Linear Regression) model. It was proposed that the use of historic data
(representing congestion) coupled with real-time schedule adherence data
(representing real-time congestion and demand inputs) resulted in better
performance of the ANN model.
Conclusions: This paper describes the results of three bus travel time prediction
algorithms which were calibrated and tested on a transit route in Houston, Texas.
The input to the models consisted of historic data (i.e., link travel time and dwell
time) and real-time schedule adherence data. It was found that the Artificial Neural
Network models (used without clustering of the data) performed considerably better
than either a historic data based model or MLR models. It was hypothesized that
ANN was able to identify the complex non-linear relationship between travel-time
and the independent variables and this led to the superior results.
Paper no.3
Title: Using bus Travel Time Data to Estimate Travel Times on Urban Corridors.
Aim: This study determines whether transit vehicles/buses can be used as probe
vehicles for collecting travel time data for automobiles on urban corridors. It
analyses the nature of information collected by the buses and develops formulas to
covert the travel time of a bus to that of an automobile. Data on bus and automobile
-
8/13/2019 Time Series Analysis Brt Delhi
15/81
travel time on various sections of arterials in the northern part of New Castle
County, Delaware was used for this purpose.
Methodology:
The tasks involved are: a) to measure the travel time of the bus and the automobile
for the same section, b) to analyze the characteristics of the components of the Bus
Travel Time (BTT) and there variability, c) to develop a model that converts the
travel time of the bus to the average travel time of the automobile, d) to verify the
model by the collected data. The procedure is to convert the BTT to the ATT
(Automobile Travel Time) that is expected before the next BTT data are updated.
The predicted travel time is assumed to be equal to the estimate obtained from the
last available BTT. It is assumed that the predicted travel time would be closer to the
actual travel time if the data is collected at shorter intervals, which depends on the
frequency of buses (or measurement intervals).
The required accuracy of the predictions was debated, since a higher accuracy
complicates the measurement plans and the procedure of conversion (BTT to
ATT).On the other hand, a much lower accuracy of prediction may render the
predictions useless. Assigning a monetary value to the travel time and value assigned
to differences between predicted and actual travel time, the tolerable error of
estimate was found to be 10% to 15% of the actual travel time. The distance over
which travel time was estimated worked out to 4.6km (assuming a 55km/hr speed
and a travel time of 5min).
The difference of BTT from the average travel time of the stream is a random
variable. Buses typically, travel in the rightmost lane of the urban corridors and this
induces a bias in the travel time of buses. However, despite the sources of
randomness and bias in the difference between ATT and BTT, buses run on heavily
travelled urban corridors (at a high frequency during peak hours), follow traffic rules
and observe speed limits. These characteristics make them attractive as probe
vehicles.
Postulating that the differences between ATT and BTT arise because of the
following factors: the stopping time of the bus at bus stops, the time lost by the bus
because of repeated accelerations and decelerations from and to a stop, basicdifference between the operating abilities of the bus and the automobile, adherence
-
8/13/2019 Time Series Analysis Brt Delhi
16/81
(by the bus and the automobile) to the posted speed limits, the tendency of the bus to
use the right lane; a simple predictive equation treating the actual running time of the
bus as an independent variable is formed. The equation was changed repeatedly
taking into account various factors: statistical importance of calibration constants,
insight provided by the calibration constants into the relation between the variables,
effort to make the model as calibration free as possible.
Results:
Five models were developed for the five sites and the results are presented in
equations (i) and (ii):
= + 0.14 (i)
For less frequently congested roads
= + (0.18) (ii)
For more frequently congested roads
Using this result, we can predict the average travel time of the automobile from the
data on the BTT and the general characteristics of the road section. Although five
sites may not be enough to develop a rule of thumb, such a rule may be developed
after the study of many more arterial sections. The use of AVL equipped buses as a
data source is promising because the measurement function is already available by
default and the task of prediction can be performed with minimum manual
intervention.
Paper No.4 :
Title: Chaotic analysis of traffic time series.
Authors: Pengjian Shang, Xuewei Li, Santi Kamae
Input Variables: Speed, volume, occupancy collected every 20s.
Aim: Paper applies non-linear time series modelling techniques to analyse the traffic
data collected from Beijing Xizhimen
Methodology: Raw data screened for errors, aggregated into 2min data, average
speed, average volume, total occupancy calculated. Draw curves for correlation
-
8/13/2019 Time Series Analysis Brt Delhi
17/81
function v/s r, range of scaling region, from this plot the chaotic nature of traffic
time series is known, the slope of the line in the scaling region is the correlation
dimension. Correlation dimension v/s Embedding dimension is plotted. Phase space
is reconstructed using "method of delays. The slope values corresponding to the
largest Lyapunov exponent were obtained after the least-squares line fit for the
average speed time series and was found to be 0.25 (deviation +- 0.02).
Results: Saturation of correlation dimension beyond a certain embedding dimension
value is an indication of the presence of deterministic dynamics, the finite and low
correlation dimension is an of the existence of deterministic dynamics. Positive
value of Lyapunov Exponent is a strong indicator of chaos.
Conclusions:Traffic time series is deterministic and can be modelled using phase
space techniques. the predicting length of the traffic time series should be about
8min.
Paper No.5
Title: Use of the Box and Jenkins Time Series Technique in Traffic forecasting
Authors: Nancy L. Nihan and K Jello O. Holmesland, Department of Civil
Engineering, University of Washington, Seattle, U.S.A.
Input Variables: Average Weekday Volume (AWD) (1968-1977)
Aim: To show the short-range accuracy of the simplest possible model, to
investigate the accuracy of the Box-Jenkins technique for short-range forecasting
(12-month forecasting period).
Methodology: 2 steps were followed: 1) data fitting,2) model selection. After
examining several models and conducting many statistical tests, ARIMA
(Autoregressive Integrated Moving Average) was finally choosen. Two types of
forecasts - a simple forecast and an adaptive forecast were made. All errors were
found to be around 5% or less.
Results: It was found that it is possible to fit an ARIMA model as well as a
multiplicative model to traffic data from the highway under consideration, using the
Box and Jenkins technique. The ARIMA model selected was only two percent awayfrom the measured values at the end of a twelve-month forecast, ARIMA is,
-
8/13/2019 Time Series Analysis Brt Delhi
18/81
therefore, highly accurate and easy to use after it has been estimated. It requires less
data input, and is flexible. It can accommodate more than one interval in complex
time series models, It can be used to relate two or more time series, and changes
taking place in a time series can be detected very soon, so it can be used as an early
warning system.
Paper No. 6.
Title: A multivariate state space approach for urban traffic flow modeling and
prediction.
Authors: Anthony Stathopoulos, Matthew G. Karlaftis, Department of
Transportation Planning and Engineering, School of Civil Engineering, National
Technical University of Athens.
Input Variables: 3-min volume measurements from urban arterial streets near
downtown Athens, flow (volume) and occupancy data used to estimate speed and
travel time.
Aim: Developing flexible and explicitly multivariate time-series state space models
using core urban area loop detector data, to model and predict flow at an urban
signalized arterial.
Methodology: Data from 144 loop locations, 5 sequential (multivariate setting)
detectors along an important 3-lane per direction signalized arterial on the periphery
of the core area of the city (Alexandras Avenue) are chosen for further analysis.
Time series is tested for stationarity using the augmented Dickey Fuller (ADF) test.
Determination of basic autoregressive and cross-correlation characteristics of the
time series for the various loop locations, State space models were developed for
both the pooled data (data from all time periods combined), and the data from the
various periods separately. The models developed were flexible for an
autoregressive and a moving average order of up to three lags. 70% of the data was
used for model development and 30% for testing.
Results: Predictions obtained from the state space models are superior to those
obtained from the ARIMA models; in one of the loops, the state space model yields
-
8/13/2019 Time Series Analysis Brt Delhi
19/81
a mean absolute percent error (MAPE) of 12% compared to a 20% MAPE value
from the ARIMA model; MAPE values reported here are rather high when compared
to values reported in other studies.
Conclusions: Despite its potential usefulness, traffic flow in signalized urban
arterials cannot be predicted, at least in the short-run, with as much accuracy as flow
in urban freeways. The results of the models developed clearly suggest that, at least
in the case of Athens, different specifications are appropriate for different time
periods. Further, it also appears that the use of multivariate state space models is
relevant in the urban roadway system.
Paper No.7.
Title: A time-series analysis of public transit ridership in Portland,Oregon,1971-
1982.
Authors: Michael Kyte, James Stoner, Jonathan Cryer.
Input Variables: level of transit service available, (2) relative costs of travel by
transit and by automobile, (3) the size of the travel market, and (4) other factors such
as gasoline shortages, weather, etc.
Aim: Comprehensive analysis of public transit usage in Portland, Oregon, from 1971
through 1982 using time-series analysis, The impacts of the 81 service changes and 5
fare changes implemented have been analyzed at both at the system and route levels
using transfer function and intervention time-series models, the effects of auto travel
costs and the local economy are included.
Methodology: A) Model development: Transfer function model is chosen and
Impact Analysis and Intervention analysis are conducted, B) forecast procedure was
accomplished by not using the final one year of data (July 1981-June 1982) for the
system data and fall 1981-spring 1982 for the route-level data), estimating the
models, and then making forecasts for 12 months or four quarters ahead. The
forecasts were then compared with the actual ridership data. C) Three levels of data
aggregation were used: system level, sector level, and route level. Three classes of
time-series models were developed including univariate transfer function,
intervention and simultaneous equations transfer function.
-
8/13/2019 Time Series Analysis Brt Delhi
20/81
Results: The effects of service-level and fare changes on transit ridership are not
instantaneous but are delayed and distributed over specific periods of time. The
existence of these lag structures is expected from consumer behavior theory. The
effect of fare changes can be measured for up to three months after their
implementation. Gasoline price and employment level changes do have immediate
effects with no discernable lag structures. Feedback relationships were identified
between transit ridership, fare, service level, and gasoline price. For example,
gasoline price changes affect future transit fare changes. The service-level and fare
elasticities computed for the system, the six sectors, and the 26 routes were in the
range reported by previous studies. Models were generally consistent, in terms of lag
structure and elasticities, between the three data aggregation levels. However, some
variables are inherently more effective at one level than another. The system models
had mean absolute percent errors (MAPE) of less than five percent.
Paper No.8.
Title: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA
Process: Theoretical Basis and Empirical Results.
Authors: Billy M. Williams and Lester A. Hoel.
Aim: To present a case for acceptance of a specific time series formulationthe
seasonal autoregressive moving average processas the appropriate parametric
model for a specific type of ITS (Intelligent Transportation System) forecast: short-
term traffic condition forecasts at a fixed location in the network, based only on
previous observations at the forecast location.
Methodology: A) theoretical justification for the application of ARIMA models:
assertion that a weekly seasonal difference will yield a stationary transformation of
discrete time traffic condition data series, coupled with the Wold decomposition
theorem, B) Hypothesis: properly fitted seasonal ARIMA models will provide
accurate traffic condition forecasts, C) Testing of hypothesis through empirical
results, where correlation analysis is shown as the basis for assessing the stationarity
of series transformations using a first weekly difference; presentation of the model-
fitting results and a discussion of the heuristic benchmarks used to assess the
predictive performance of the fitted seasonal ARIMA models.
-
8/13/2019 Time Series Analysis Brt Delhi
21/81
Results: One-step seasonal ARIMA predictions consistently outperformed heuristic
forecast benchmarks. Assertions and findings presented in this paper directly
contradict a statement in Kirby et al. 1997, namely that extending simple ARIMA
models to include seasonal and other effects, in practice... did not have a
substantial impact on the results. Theoretical foundation for seasonal ARIMA
modeling negates any theoretical motivation to investigate high level nonlinear
mapping approaches, such as neural networks. This assertion is supported by
comparison to actual neural network forecasting results with a common data set.
Paper No.9
Title: Multivariate Short-Term Traffic Flow Forecasting Using Time-Series
Analysis.
Authors: Bidisha Ghosh, Biswajit Basu and Margaret OMahony.
Input Variables: traffic flow, number of maneuvers, time.
Aim: Introducing a different class of time-series models called structural time-series
model (STM) (in its multivariate form) to develop a parsimonious and
computationally simple multivariate short-term traffic condition forecasting
algorithm.
Methodology: A) A "seemingly unrelated time-series equation (SUTSE) Model,
which is also a multi-inputmulti-output short-term traffic flow forecasting model,
where the number of input intersections is more than number of output intersections
is choosen. B) The proposed multivariate SUTSE traffic flow forecasting
methodology is applied to a congested urban transportation network at the city center
of Dublin. A network of ten intersections within the transport network is chosen for
this purpose. C)The univariate traffic flow observations obtained over each 15-min
interval from the inductive loop detectors situated at these ten intersections and their
nearest available upstream junctions are modeled using the proposed multivariate
traffic flow model. D) The cross-correlational structure of the ten chosen junctions is
verified. E) Traffic flow time series is reduced to stationary form by 'differencing,
second-order stationarity of the time-series data sets used in the MST model are
checked by plotting the autocorrelation functions (ACFs) of the data sets, To ensure
stationarity, seasonal differencing has been performed on all the modeled traffic flow
-
8/13/2019 Time Series Analysis Brt Delhi
22/81
time-series data sets. All of the ten series of traffic flow observations are modeled
using homogeneous SUTSE models.
Results: Hyperparameter estimates and the plot of the seasonal component show
that the seasonality is deterministic in nature. The trend component is stochastic and
depicts the within-day local fluctuations in the data. The trend component varies
about a zero mean value, validating the assumption that there is no slope component
latent within the traffic flow data set. The SUTSE model is computationally more
efficient than some of the other existing multivariate short-term traffic flow
forecasting methodologies.The checking for stationarity conditions is not critical to
the development of the model. The developed SUTSE model can separately trace the
evolution of each individual component (trend, seasonality, etc.) of the traffic flow
data over time. Consequently, the deterministic nature of the seasonal component of
the traffic volume observations from junctions at urban signalized arterials has been
established.MST model can additionally include the effect of changes in traffic
conditions at one or more immediate upstream junctions to improve the predictions
at the downstream output junction.
Paper No.10.
Title: Travel Time Prediction using a Seasonal Autoregressive Integrated Moving
Average Time Series Model.
Authors: Angshuman Guin.
Input Variables: Volume, average speed and lane-occupancy data.
Aim: Investigating the possibility of extending the Box and Jenkins technique to
develop a Seasonal ARIMA (also sometimes referred to as SARIMA) prediction
model for travel times.
Methodology: Average speed converted into travel time, several weeks travel time
data is plotted over time, plots are superimposed and the periodicity is detected, The
ACF( Autocorrelation Function) plot of the raw travel time data for all weekdays is
plotted, the ACF plot of the single lag (15-minute interval) differenced travel time
data is plotted, the ACF plots for a dataset with just the Mondays of the consecutive
weeks is plotted, ACF of Raw Monday Travel Time Data (10 days) is plotted tocheck for stationarity, the first difference of the series is plotted but it does not yield
-
8/13/2019 Time Series Analysis Brt Delhi
23/81
any stationarity, the autocorrelation plots indicate that a seasonal differencing at
weekly level would generate a stationary series. A multiplicative seasonal
autoregressive integrated moving average process of period s, with regular and
seasonal AR (Autoregressive) orders p and P, regular and seasonal MA orders q and
Q, and regular and seasonal differences ,is referred to as an ARIMA (p,d,q)(P,D,Q)s
model.
Conclusions: Travel times have strong weekly seasonality, such seasonality is to be
expected at higher levels of aggregation and not in the system level data at which the
detectors record the data, weekly periodicity can be successfully used in a predictive
model for segment travel times, this model is expected to provide effective travel
time forecasts irrespective of whether the travel time estimates are based on point
detection data, probe vehicle data or Automatic Vehicle Identification (AVI) data.
Relevance of Literature review:
Paper no.3 , Using bus Travel Time Data to Estimate Travel Times on Urban
Corridors which is included in the literature review is particularly useful because it
considers the use of data collected from Buses fitted with AVL (Automated Vehicle
Location) equipment for estimating travel time for motor vehicles on the same
routes. The data being used in this project has been collected in a similar manner
from buses fitted with GPS equipment. Hence, the models/methodology used in the
papers can be used to see how the data available with us can be used to estimate
travel times for all vehicles on the routes covered.
-
8/13/2019 Time Series Analysis Brt Delhi
24/81
CHAPTER - 3
DATA COLLECTION AND ANALYSIS
3.1 Description of Available Data:
Data Set 1:
The data has been collected over a period of 1 week for Route 419(BRT corridor)
The data has been collected using GPS enabled AVL ( Automated Vehicle
Location) project implemented by DIMTS (Delhi Integrated Multi-Modal TransitSystem Ltd) that record certain parameters at every 10 seconds for every bus.
The parameters recorded are: latitude, longitude, timestamp, speed, distance
travelled by object, user_id( registration of the bus).
The available data covers the 24 hour schedule of the bus. The bus route generally
varies from 9.30 am in the morning to 7pm in the evenings. Since the buses return do
circular routes, the data for the return journey is not recorded by the GPS.
Dataset 2:
The GPS data over various bus routes of Delhi collected from 1-1-2013 to 31-1-
2013.
The parameters recorded are: latitude, longitude, timestamp, speed, distance
travelled by object, user_id( registration of the bus).
The data is already separated into UP and DOWN trips for all routes.
3.2 Treatment of Dataset 1:
The data available was present in the form of Latitude and Longitude and time
stamp, hence the first step was the conversion of these to distance and speed values.
Using the spherical law of cosines formula (equation (iii)) distance is calculated
from latitude, longitude and R (radius of earth);
http://mathworld.wolfram.com/SphericalTrigonometry.htmlhttp://mathworld.wolfram.com/SphericalTrigonometry.html -
8/13/2019 Time Series Analysis Brt Delhi
25/81
Spherical law of cosines:
= (acossin1 .sin2+ cos1 . cos2 . cos (iii)
3.3 Stationarity:
A stationary time series is one whose statistical properties such as mean, variance,
autocorrelation, etc. are all constant over time. Most statistical forecasting methods
are based on the assumption that the time series can be rendered approximately
stationary (i.e., "stationarized") through the use of mathematical transformations. A
stationarized series is relatively easy to predict: you simply predict that its statistical
properties will be the same in the future as they have been in the past.
Another reason for trying to stationarize a time series is to be able to obtain
meaningful sample statistics such as means, variances, and correlations with other
variables. Such statistics are useful as descriptors of future behavior onlyif the series
is stationary. For example, if the series is consistently increasing over time, the
sample mean and variance will grow with the size of the sample, and they will
always underestimate the mean and variance in future periods. And if the mean and
variance of a series are not well-defined, then neither are its correlations with other
variables. For this reason it is important to be cautious about trying to
extrapolate regression models fitted to non-stationary data.
First Difference:
The first difference of a time series is the series of changes from one period to the
next. If Y(t) denotes the value of the time series Y at period t, then the first
difference of Y at period t is equal to Y(t)-Y(t-1). In Statgraphics, the first difference
of Y is expressed as DIFF(Y). If the first difference of Y is stationary and
also completely random(not autocorrelated), then Y is described by a random
walk model: each value is a random step away from the previous value. If the first
difference of Y is stationary but not completely random--i.e., if its value at period t is
autocorrelated with its value at earlier periods--then a more sophisticated forecasting
model such as exponential smoothing or ARIMA may be appropriate. (Note: ifDIFF(Y) is stationary and random, this indicates that a random walk model is
http://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htm -
8/13/2019 Time Series Analysis Brt Delhi
26/81
appropriate for the original series Y, not that a random walk model should be fitted
to DIFF(Y). Fitting a random walk model to Y is logically equivalent to fitting a
mean (constant-only) model to DIFF(Y)).
Test for Stationarity:
If the series has a stable long-run trend and tends to revert to the trend line following
a disturbance, it may be possible to stationarize it by de-trending (e.g., by fitting a
trend line and subtracting it out prior to fitting a model, or else by including the time
index as an independent variable in a regression or ARIMA model), perhaps in
conjunction with logging or deflating. Such a series is said to be trend-
stationary. However, sometimes even de-trending is not sufficient to make the
series stationary, in which case it may be necessary to transform it into a series of
period-to-period and/or season-to-season differences. If the mean, variance, and
autocorrelations of the original series are not constant in time, even after detrending,
perhaps the statistics of the changes in the series between periods or between
seasons will be constant. Such a series is said to be difference-
stationary. (Sometimes it can be hard to tell the difference between a series that is
trend-stationary and one that is difference-stationary, and a so-called unit root
testmay be used to get a more definitive answer.
3.4 Methodology of Project (for DataSet-1)
1) Testing for Stationarity :-KPSS Test (KwiatkowskiPhillipsSchmidtShin Test)
-ADF Test (Augmented DickeyFuller test)
2) Trend AnalysisDefinition of Non-Stationarity:
A Unit root test tests whether atime series variable is non-stationary using
anautoregressive model. A well-known test that is valid in large samples is
theaugmented DickeyFuller test.The optimal finite sample tests for a unit root in
autoregressive models were developed by John Denis Sargan andAlok Bhargava.
These tests use the existence of aunit root as thenull hypothesis.
http://en.wikipedia.org/wiki/Time_serieshttp://en.wikipedia.org/wiki/Autoregressivehttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/John_Denis_Sarganhttp://en.wikipedia.org/wiki/Alok_Bhargavahttp://en.wikipedia.org/wiki/Unit_roothttp://en.wikipedia.org/wiki/Null_hypothesishttp://en.wikipedia.org/wiki/Null_hypothesishttp://en.wikipedia.org/wiki/Unit_roothttp://en.wikipedia.org/wiki/Alok_Bhargavahttp://en.wikipedia.org/wiki/John_Denis_Sarganhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Autoregressivehttp://en.wikipedia.org/wiki/Time_series -
8/13/2019 Time Series Analysis Brt Delhi
27/81
ADF Test:
An Augmented DickeyFuller test (ADF) is an augmented version of theDickey
Fuller test for a larger and more complicated set of time series models. The
augmented DickeyFuller (ADF) statistic, used in the test, is a negative number. The
more negative it is, the stronger the rejection of the hypothesis that there is a unit
roots at some level of confidence.
3.5 Results of Application of ADF Test on Data:
The ADF test was applied on the data for each run (the bus performs 4 runs in a
day), and the results obtained were compiled in Table 2 and were compared to the
critical values for DF (Dickey-Fuller) and ADF (Augmented Dickey-Fuller) tests
provided in Table 1.The ADF values were greater than the critical values for the test.
Table 1: Critical values for DF and ADF Tests (Fuller, 1976, p373)
Significance level 10% 5% 1%
C.V. for constant but no trend -2.57 -2.86 -3.43
C.V. for constant and trend -3.12 -3.41 -3.96
http://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_test -
8/13/2019 Time Series Analysis Brt Delhi
28/81
Table 2. Compiled Results of ADF test on Dataset 1:
S.No
.
Type
ADF test
statistic
1%
-3.96
5%
-3.41
10%
-3.12
Durbin
Watson
Statistic
1 1strun,1stdifference,trend+in
tercept,lag 10 -8.625 -3.982 -3.421 -3.133 1.996
2 2n -run,1stdifference,
trend+intercept., lag 10 -9.545 -3.983 -3.422 -3.134 2.003
3 3r
-run,1st-difference,
trend+intercept, lag 10 -10.202 -3.987 -3.424 -3.135 1.998
4 4th run,1st difference, lag
10 -11.630 -3.984 -3.422 -3.134 1.997
5 1st run,1st difference, trend
only, lag 10 -9.558 -3.448 -2.869 -2.570 2.003
6 1strun,2n -difference,
trend+intercept, lag 10 -10.559 -3.982 -3.421 -3.133 2.016
(Test results in CD-Appendix 1)
3.6 Interpretation of results:
Since the ADF value is always greater than the critical values, hence the null
hypothesis is rejected. The series is stationary.
-
8/13/2019 Time Series Analysis Brt Delhi
29/81
Interpreting the DurbinWatson statistic:
Since d (DurbinWatson statistic) is approximately equal to 2(1 r), where r is the
sample autocorrelation of the residuals, d = 2 indicates no autocorrelation. The value
of d always lies between 0 and 4. If the DurbinWatson statistic is substantially less
than 2, there is evidence of positive serial correlation. As a rough rule of thumb, if
DurbinWatson is less than 1.0, there may be cause for alarm. Small values
of d indicate successive error terms are, on average, close in value to one another, or
positively correlated. If d > 2 successive error terms are, on average, much different
in value to one another, i.e., negatively correlated. In regressions, this can imply an
underestimation of the level ofstatistical significance.
The DurbinWatson statistic approaches 2 in most of the tests, suggesting that
autocorrelation does not exist.
3.7 Dataset-2
Description of data:
The data provided by DIMTS was 1 month (1/1/13 to 31/1/13) data for different bus
routes of Delhi.
Methodology:
The bus routes were divided into segments, using Bus stops, Intersections (3
way/4way) and Roundabouts as Segment ends. The mean speed for each segment
was calculated and these were tabulated to see trends over time. The speeds are
tabulated as Results according to Day of week and according to Hourly Time Slot
(Appendix A of CD). A graph of each table is added at the end. The Bottlenecks
exposed via these graphs (segments having low speeds) are tabulated in
Conclusionstables. The range of speed for each Time slot and Day for each route
is also provided in the Conclusions Table.
Procedure:
The data was provided by DIMTS as Combined Data file per route. This was divided
into separate files (using MATLAB software) for separate days and for separate
buses. Speed calculations were carried out on these to determine the instantaneous
http://en.wikipedia.org/wiki/Statistical_significancehttp://en.wikipedia.org/wiki/Statistical_significance -
8/13/2019 Time Series Analysis Brt Delhi
30/81
speed along the routes. The routes were divided into segments according to
occurrence of bus-stop/traffic light/intersection and the mean speed was calculated
for each segment. The results for a particular time slot for many buses for a
particular segment were averaged to give an estimate of mean speed at a particular
segment of a route at a particular time (1 hour time slot).These were aggregated
according to day of week. By this, a speed profile of every day of the week was
obtained for all the routes studied.
Following are the Route Data, Segment ends and Conclusions for each route:
-
8/13/2019 Time Series Analysis Brt Delhi
31/81
Route: 108 Up/Down:
The route lies between the ends Hari Nagar Clock Tower and Nehru Vihar.Table
3 summarizes the Route Characteristics.
Table 3: Route Characteristics for Route 108
Route No. 108
Depot:- Low Floor
RUNNING TIME:- 64 Minutes
Departure Time
Hari Nagar. Clock Tower Nehru Vihar
0536 1048 1624 0648 1200 1736
0552 1104 1640 0704 1216 1752
0608 1120 1656 0720 1232 1808
0616 1128 1704 0728 1240 1816
0624 1136 1712 0736 1248 1824
0640 1152 1728 0752 1304 1840
0656 1208 1744 0808 1320 1920
0712 1224 1800 0824 1336 1936
0800 1400 1848 0904 1512 2024
0816 1416 1928 0928 1528 2040
0832 1432 1944 0944 1544 2056
0840 1440 1952 0952 1552 2104
0848 1448 2000 1000 1600 2112
0904 1504 2016 1016 1616 2128
0920 1520 2032 1056 1632 2144
0936 1536 2048 1112 1648 2200
-
8/13/2019 Time Series Analysis Brt Delhi
32/81
Fig 1 : Route 108 UP
3.7.1 108 UP (Nehru Vihar Road to Hari nagar Crossing)
The route is divided into segments, the segments being separated by Bus Stopping
points like Bus stops, Intersections (3 way/4 way, Roundabout)( Table 4).The
Bottlenecks identified for different time slots and days are summarized in Table 5.
-
8/13/2019 Time Series Analysis Brt Delhi
33/81
Table 4: Segments of Route for analysis:
1 Nehru vihar Road 28.71015 77.22426
2 Fourway 28.70975 77.22698
3 Nehru vihar crossing 28.70885 77.22599
4 Police station timarpur 28.70686 77.22412
5 Balak ram hospital 28.70505 77.22322
6 Timar pur 28.70061 77.22125
7 Timarpur water tank 28.69746 77.2206
8 North mall road 28.69428 77.21986
9 Mall road 28.69342 77.21887
10 International students hostel 28.69608 77.21178
11 Fourway 28.69652 77.2108
12 Khalsa college 28.69511 77.20993
13 Patel chest(4 way) 28.69175 77.20827
14 Sri ram college 28.68906 77.20697
15 Daulat ram college 28.68805 77.20646
16 Maurice nagar 28.68676 77.20586
17 Roop nagar 28.68462 77.20369
18 Roop nagar 28.68413 77.20250
19 Kamla nagar 28.68320 77.20108
20 Nangia park(roundabout) 28.67991 77.19585
21 Intersection 28.67772 77.18900
22 Leela vati vidya mandir 28.67712 77.18904
23 Gulabi bagh crossing 28.67516 77.18870
24 Shastri nagar 28.67261 77.18622
25 Gulabi bagh 28.67097 77.18412
26 Fourway 28.67007 77.18349
27 DDA flats sarai basti 28.67097 77.17615
28 Shiv mandir 28.67160 77.17313
29 Inderlok 28.67206 77.16894
30 Intersection 28.67079 77.16655
31 Zakhira road 28.66876 77.16523
-
8/13/2019 Time Series Analysis Brt Delhi
34/81
32 Zakhira 28.66759 77.16421
33 Roundabout 28.66680 77.16387
34 DCM chemicals 28.66521 77.15986
35 Campa cola 28.66199 77.1521136 ESI dispensary 28.66081 77.14885
37 Intersection 28.65961 77.14603
38 Moti nagar market 28.65733 77.14178
39 Threeway 28.65456 77.13680
40 Roundabout 28.65286 77.13842
41 F block 28.65150 77.14076
42 Kirti nagar ps 28.64952 77.1438243 Furniture market 28.64764 77.14236
44 Saraswati garden 28.64600 77.14028
45 Wood market 28.64279 77.13713
46 Man sarovar garden 28.63815 77.13241
47 Fourway 28.63754 77.13065
48 Mayapuri depot 28.63670 77.12868
49 Govt. press mayapuri 28.63432 77.1277750 Maya puri metal forging 28.63087 77.12479
51 LIG flats 28.63077 77.11937
52 Swarag ashram 28.63088 77.11519
53 Beri-wala bagh 28.63103 77.11247
54 Round-about 28.63160 77.11163
55 DDU hospital 28.62824 77.11103
56 Hari nagar clock tower 28.62469 77.11039
-
8/13/2019 Time Series Analysis Brt Delhi
35/81
Table 5. Conclusions about Route 108Up
Slot/Day: Bottleneck: Mean speed:
Sundays 1) Nehru Vihar road to Four way (0.93kmph) 9.02kmph to
35.50kmph
Mondays 1) Four way after Mayawati Garden to Mayapuri
depot (4.91kmph)
6.95kmph to
35.30kmph
Tuesdays 1) Mayapuri depot to Govt. press mayapuri
(6.9kmph)
9.20kmph to
30.80kmph
Wednesdays 1) Four way after International students hostel to
Khalsa College (5.37kmph)
2) Harinagar clock tower(1.37kmph)
8.50kmph to
35.46kmph
Thursdays 1) Leelawati vidya mandir to Gulabi bagh crossing
(4.44kmph)
2) Four way after man sarovar garden to Mayapuri
depot (5.75kmph)
8.67kmph to
32.50kmph
Fridays 1) Kamlanagar to Nangia park(roundabout)
(4.25kmph)2) Harinagar clock tower(4.5kmph)
8.42kmph to
35.01kmph
Saturdays 1) Four way after Mansarovar garden to Mayapuri
depot(5.96kmph)
2) Harinagar clock tower(5.15kmph)
9.26kmph to
34.90kmph
8am-9am 1) Four way after Mansarovar garden to Mayapuri
depot(5.14kmph)
2) Harinagar clock tower(4.5kmph)
10.83kmph to
37.20kmph
9am-10am 1) Four way after Mansarovar garden to Mayapuri
depot(6.06kmph)
9.10kmph to
34.90kmph
10am-11am 1) Four way after Mansarovar garden to Mayapuri
depot(4.46kmph)
9.08kmph to
33.87kmph
11am-
12noon
1) Four way after Mansarovar garden to Mayapuri
depot(5.68kmph)
2) Harinagar clock tower (5.41kmph)
9.30 kmph to
34.37kmph
12noon-1pm 9.79kmph to
-
8/13/2019 Time Series Analysis Brt Delhi
36/81
32.46kmph
1pm-2pm 1) Khalsa College to Patel chock (5.37kmph)
2) Nangia Park to Intersection(5.57kmph)
8.76kmph to
32.69kmph
2pm-3pm 1) Four way after Mansarovar garden to Mayapuridepot(4.91kmph)
8.54kmph to30.56kmph
3pm-4pm 1) Kamlanagar to Nangia park(5.91kmph) 8.43kmph to
32.55kmph
4pm-5pm 1) Nehru vihar road to Four way(1.11kmph)
2) Kamlanagar to Nangia park( roundabout)
3) Four way after Mansarovar garden to Mayapuri
depot(4.91kmph)
8.06kmph to
34.15kmph
5pm-6pm 1) Nehru vihar road to Four way(1.58kmph)
2) Kamlanagar to Nangia park(5.10kmph)
3) Four way after Mansarovar garden to Mayapuri
depot(5.96kmph)
7.28kmph to
33.45kmph
6pm-7pm 1) Nehru vihar road to Four way(1.42kmph)
2) Kamlanagar to Nangia park(4.27kmph)
7.58kmph to
31.36kmph
7pm-8pm 1) Nehru Vihar to Four way(0.93kmph)
2) Kamlanagar to Nangia park(4.25kmph)
3) Leelawati vidya mandir to Gilabi bagh
crossing(4.44kmph)
4) Four way after Mansarovar garden to Mayapuri
depot(5.75kmph)
7.82kmph to
34.38kmph
-
8/13/2019 Time Series Analysis Brt Delhi
37/81
3.7.2 Route: 108 Down
The route lies between the ends DDU Hospital and Balak Ram Hospital(Fig2).The
route is divided into segments, the segments being separated by Bus Stopping points
like Bus stops, Intersections (3 way/4 way, Roundabout)( Table 6).The Bottlenecks
identified for different time slots and days are summarized in Table 7.
Fig2: Route 108 Down
Table 6. Segments for Route 108 Down:
S.No.
Segment
from/to -> Latitude Longitude S.No Segment from/to -> Latitude Longitu
1 DDU hospital 28.62805 77.11088 27 Inderlok 28.67142 77.167
2 Roundabout 28.63170 77.11136 28 Fourway 28.67253 77.1694
3 Beri wala bagh 28.63147 77.11202 29 Shiv mandir 28.67186 77.173
4 Swarg asharam 28.63097 77.11485 30 Shastri nagar E block 28.67126 77.176
5 Swarg asharam 28.63101 77.11510 31 Fourway 28.67003 77.1832
6 LIG flats 28.63091 77.11946 32 Gulabi bagh 28.67106 77.183
7
(Four - way)
Ram singh 28.62992 77.12359 33
Shastri nagar A
block 28.67284 77.186
-
8/13/2019 Time Series Analysis Brt Delhi
38/81
marg
8
Maya puri
metal forging 28.63064 77.12427 34 Gulabi bagh crossing 28.67489 77.188
9 Govt press 28.63512 77.12792 35Swami narayanmarg(Three way) 28.67552 77.188
10 Fourway 28.63743 77.12977 36 Leelawati mandir 28.67706 77.188
11
Man sarovar
garden 28.63843 77.13249 37 Roundabout 28.67927 77.1938
12 Chuna bhati 28.63911 77.13343 38 Fourway 28.68142 77.1980
13 Wood market 28.6426 77.13673 39 Kamla nagar 28.68239 77.1994
14
Saraswati
garden 28.64608 77.14004 40
Roop nagar(Bus Stop
befpre roundabout) 28.68409 77.202
15
Furniture
market 28.6478 77.14225 41
Roop nagar(Bue Stop
after roundabout) 28.6847 77.203
16 Kirti nagar 28.6497 77.14374 42 Maurice nagar 28.68629 77.205
17
F block kirti
nagar 28.65138 77.14079 43 Sri ram college 28.68893 77.206
18 Rounabout 28.65245 77.13908 44 Patel chest 28.69144 77.207
19 Kirti nagar 28.65483 77.13681 45 Patel chest 28.69228 77.208
20
Moti nagar
market 28.65721 77.14111 46 Khalsa college 28.69446 77.209
21 Moti nagar 28.65882 77.14419 47 Threeway 28.6965 77.2105
22 ESI dispensary 28.66047 77.14731 48
International students
hostel 28.69625 77.211
23 Campa cola 28.66273 77.15335 49 Entrance to side road 28.69532 77.214
24 DCM chemicals 28.66521 77.15943 50
Lucknow road govt
school 28.69577 77.216
25 Roundabout 28.66649 77.16335 51 Lucknow road 28.69686 77.2166
26 Zakhira 28.66766 77.16407 52 Balak ram hospital 28.70521 77.223
-
8/13/2019 Time Series Analysis Brt Delhi
39/81
Table 7. Conclusions for Route 108 Down:
Slot/Day: Bottleneck: Mean speed:
Sundays 1) Govt. press to Fourway
2) Roundabout after Leelawati mandir to following
Four - way(
-
8/13/2019 Time Series Analysis Brt Delhi
40/81
(4.52kmph)
2) Four - way to Kamlanagar (5.9kmph)
34.09kmph
2pm-3pm 1) Four - way to Man sarovar garden (6kmph)
2) Four - way to Kamlanagar (5.61kmph)
9.44kmph to
30.80kmph
3pm-4pm 1)Roundabout to Four - way (4.24kmph)
2) Four - way to Kamlanagar(6.26kmph)
9.92kmph to
31.28kmph
4pm-5pm 1) Roundabout to Four - way(6.67kmph)
2) Four - way to Kamlanagar(5.65kmph)
11.10kmph to
32.25kmph
5pm-6pm 1) Four - way to Mansarovar garden (6.8kmph)
2) Roundabout to Four - way (4.25kmph)
9.89kmph to
31.80kmph
6pm-7pm 1) Four - way to Kamlanagar(6.14kmph)
2) Kamlanagar to Roopnagar(6.16kmph)
8.80kmph to
31.50kmph
7pm-8pm 1) Four - way to Mansarovar garden (6.27kmph)
2) Motinagar to ESI dispensary (4.9kmph)
3) Roundabout after Leelawati mandir to Four - way
(5.2kmph)
7.11kmph to
35.60kmph
Route: 185
The route lies between the ends Nathupura and Kendriya Terminal. Route characteristics are
summarized in Table 8.
Table 8: Route Characteristics for Route 185
Route No. 185
Depot:- Standard Floor
Running Time:-90 Mintues Nathu Pura to Kendriya Tr., Nathu Pura
to I.S.B.T. 60 Minutes
Departure Time
Nathu Pura I.S.B.T. Kend. Terminal
0500 1511 0605 1524 0924
0605 1524 0710 1603 0950
0631 1550 0736 1616 1021
0710 1611 0815 1655 1125
-
8/13/2019 Time Series Analysis Brt Delhi
41/81
0749 1620 0905 1747 1704
0800 1629 0933 1813 1746
0815 1642 1020 1905 1804
0828 1655 1051 2010 18300841 1708 1115 2023 1856
0946 1721 1209 2045
0950 1800 1230 2049
1010 1905 1255 2110
1104 1918 1335 2141
1121 1938 1419 2205
1151 1944 1445 22201230 1950 1458 2233
1314 2005 1511 2305
1340 2036
1353 2100
1406 2115
1419 2128
1458 2200
-
8/13/2019 Time Series Analysis Brt Delhi
42/81
3.7.3 Route: 185 UP
The route is from Kendriya Terminal to Nathupura(Fig 3). The route is divided into
segments, the segments being separated by Bus Stopping points like Bus stops,
Intersections (3 way/4 way, Roundabout)(Table 9).The Bottlenecks identified for
different time slots and days are summarized in Table 10.
Figure 3 : Route 185 Up
-
8/13/2019 Time Series Analysis Brt Delhi
43/81
Table 9: Segments for 185 Up
S.No.
Segment from/to-
> Latitude Longitude
Segment from/to-
> Latitude Longitude
1
Kendriya
terminal 28.61744 77.20421 35
Civil lines metro
station 28.67641 77.22499
2 Roundabout 28.61739 77.20555 36 IP College 28.68005 77.22356
3
Kendriya
terminal 28.61951 77.20612 37
Three -
way(Mahatma
Gandhi road) 28.68127 77.22274
4
Kendriya
terminal 28.62167 77.20623 38 Old Secretariat 28.68407 77.222095 NDPO 28.62559 77.20649 39 Khyber Pass 28.69017 77.22118
6 Roundabout 28.62653 77.20747 40
Three - way(north
mall road) 28.69318 77.21982
7
Gurudwara
bangle sahib 28.62546 77.20938 41 Mall Road 28.69348 77.21887
8 YMCA 28.62623 77.21243 42
International
Students Hostel 28.69612 77.21182
9 Jantar Mantar 28.62790 77.21596 43 GTB nagar 28.69828 77.20605
10 Palika Kendra 28.62872 77.21652 44
Three -
way(Mahatma
gandhi marg) 28.69874 77.20483
11 Regal Cinema 28.63073 77.21742 45 Camp Chock 28.69928 77.20482
12
Three -
way(Panchkurian
road joins inner
circle) 28.63423 77.21697 46 T.B.Hospital 28.70052 77.20508
13 Shivaji Park 28.63857 77.22388 47 Gandhi Ashram 28.70466 77.20510
14
New Delhi
Railway Station 28.64145 77.22606 48 Daka Village 28.70710 77.20461
15 Roundabout 28.64241 77.22680 49
Permanand
Crossing 28.70870 77.20452
16 JL Nehru Marg 28.64207 77.22812 50 Radio Colony 28.71123 77.20436
-
8/13/2019 Time Series Analysis Brt Delhi
44/81
17
Zakir Hussain
College 28.64116 77.23001 51 Nirankar Colony 28.71458 77.20402
18
Three -
way(jawahar lal
nehru marg) 28.64074 77.23079 52
CB Raman IIT
Colony 28.72103 77.19988
19 Asaf Ali College 28.64202 77.23281 53
Nirankari Sarovar
Burari Crossing 28.72703 77.19780
20
Delhi Nagar
Nigam 28.64122 77.23520 54 Nirankari Sarovar 28.72736 77.19783
21 Hotel Broadway 28.64086 77.23838 55
Four - way(outer
ring road) 28.72808 77.19787
22 Darya Ganj 28.64319 77.24036 56
Transport
Authority 28.73074 77.19837
23 Subhash Park 28.64899 77.23957 57 Jharoda Diary 28.73491 77.19745
24 Three - way 28.64971 77.23905 58 St. Nagar 28.73828 77.19744
25 Jama Masjid 28.65124 77.23790 59 Bengali Colony 28.73871 77.19756
26 Red Fort 28.65365 77.23639 60 Francis School 28.74477 77.19802
27
Four - way(
shyam prasad
mukherjee marg) 28.65958 77.23631 61
Sarvodaya
Vidyalay Burari 28.74897 77.19841
28 GPO 28.66180 77.23479 62 Burari Village 28.75331 77.19881
29 GGS University 28.66534 77.23012 63 Burai Ghari 28.75755 77.19560
30 ISBT 28.66840 77.22757 64 Laxmi vihar 28.76002 77.19093
31
Three - way(lala
hardev sahai
marg) 28.66845 77.22660 65 Kaushik Enclave 28.76080 77.18918
32
Three - way
(ISBT) to Shyam
nath marg 28.66881 77.22659 66 Amrit Vihar 28.76413 77.18379
33 Ludlow Castle 28.67211 77.22593 67 Nathupura 28.76894 77.18060
34
Three -
way(Shyam nath
marg) 28.67304 77.22568
-
8/13/2019 Time Series Analysis Brt Delhi
45/81
Table 10. Conclusions about Route 185 UP:
Slot/Day: Bottleneck: Mean speed:
Sundays 1) Transport Authority to Four - way(5.36kmph)2) Intersection after DTC ambedkar terminal to Delhi
gate(6.08kmph)
3) Kendriya Terminal(8.44kmph)
10.08kmph to
36.87kmph
Mondays 1) Nathupora to Amrit vihar colony(1.01kmph)2) Kaushik enclave to Laxmi vihar(5.09kmph)3) Transport Authority to Four way(4.10kmph)4) Intersection(Jawaharlal Nehru marg) to Delhi
Gate(5.94kmph)
5) Three - way(outer circle,Barakhamba road) to Three -way(outer circle,Kasturba Gandhi marg)(5.59kmph)
6) Kendriya terminal Bus Stop to Kendriya terminal(5.01kmph)
8.66kmph to
36.09kmph
Tuesdays 1) Nathupora to Amrit Vihar Colony(3.10kmph)2) Four - way after Transport Authority to Burari
Crossing(4.64kmph)3) ISBT to Yamuna Bazaar(4.02kmph)4) Intersection(Jawahar lal Nehru marg) to Delhi
Gate(3.56kmph)
5) Delhi Gate to LNJP hospital(5.37kmph)6) Three - way(outer circle,Barakhamba road) to Three -
way(outer circle,Kasturba bagndhi marg)(3.04kmph)
7) St.Columbus School to Kendriya Terminal(3.72kmph)
8.82kmph to
39.84kmph
Wednesdays 1) Nathupora to Amrit Vihar Colony(5.59kmph)2) Transport Authority to Four way(3.07kmph)3) Intersection(Jawahar lal nehru marg) to Delhi
Gate(4.96kmph)
4) Kendriya Terminal (6.0kmph)
9.21kmph to
41.20kmph
Thursdays 1) Transport Authority to Four - way(3.77kmph)2) Intersection(Jawahar lal Nehru marg) to Delhi
7.23kmph to
37.76kmph
-
8/13/2019 Time Series Analysis Brt Delhi
46/81
gate(4.67kmph)
3) Three - way(outer circle,barakhamba road) to Three -way(outer circle,Kasturba Gandhi marg)(6.05kmph)
4) Kendriya terminal(0kmph)Fridays 1) Nathupora to Amrit Vihar Colony(6.26kmph)
2) Transport Authority to Four - way(4.09kmph)3) Intersection(Jawahar lal Nehru marg to Delhi
gate(5.14kmph)
4) Four - way( maharaja ranjit singh marg) to Statesmanhouse(6.41kmph)
5) Three - way(outer circle,barakhamba road) to Three -way(outer circle,kasturba Gandhi marg)(7.64kmph)
6) Three - way( near Janpath) to Three - way(outercircle,Sansad Marg)(6.24kmph)
7) Kendriya Terminal(4.43kmph)
8.82kmph to
39.06kmph
Saturdays 1) Nathupora to Amrit Vihar Colony(4.87kmph)2) Transport Authority to Four - way(4.05kmph)3) Intersection(Jawahar lal Nehru marg) to Delhi
Gate(6.83kmph)
4) Statesman house to Three - way(outercircle,Barakhamba road)(3.91kmph)
5) Kendriya terminal(3.49kmph)
8.87kmph to
36.94kmph
8am-9am 1) New Delhi Railway station to Roundabout(7kmph)2) Subhash Park to Three - way(7.63kmph)3) Four way(outer ring road) to Transport
Authority(0.44kmph)
4) Sarvodaya Vidyalaya Burari to BurariVillage(5.6kmph)
5.00kmph to
39.30kmph
9am-10am 1) New Delhi Railway Station to Roundabout(6.98kmph)2) Three - way after Subhash Park to Jama
Masjid(6.35kmph)
3) GPO to GCS university(6.72kmph)4) Four - way(outer ring road) to Transport
7.79kmph to
34.15kmph
-
8/13/2019 Time Series Analysis Brt Delhi
47/81
Authority(4.6kmph)
5) Transport Authority to Jharoda Diary(3.41kmph)10am-11am 1) Kendriya Terminal to Roundabout(1.53kmph)
2) Three - way after Subhash park to JamaMasjid(7.12kmph)
1.53kmph to
37.55kmph
11am-
12noon
1) Kendriya Terminal to Roundabout(1.16kmph)2) Subhash Park to Three - way(2.26kmph)3) International Students Hostel to GTB nagar(7.55kmph)4) Permanand Crossing to Radio Colony(5.57kmph)5) Four -way(outer ring road) to Transport
Authority(3.48kmph)
2.29kmph to
35.38kmph
12noon-1pm 1) New Delhi Railway Station to Roundabout(5.19kmph)2) Subhash Park to Three - way(2.53kmph)3) Four - way(shyam Prasad Mukherjee marg) to
GPO(5.25kmph)
4) Permanand Crossing to Radio Colony(2.62kmph)5) Four - way(outer ring road) to Transport
Authority(3.28kmph)
6) Nathupura(0kmph)
4.14kmph to
38.32kmph
1pm-2pm 1) Darya Ganj to Subhash Park(2.16kmph)2) Jama Masjid to Red Fort(5.34kmph)3) Camp Chock to TB Hospital(4.81kmph)4) Permanand Crossing to Radio Colony(3.70kmph)5) Four - way(outer ring road) to Transport
Authority(3.37kmph)
6) Nathupora(0kmph)
3.63kmph to
34.78kmph
2pm-3pm 1) Subhash Park to Three - way(3.02kmph)2) Jama Masjid to Red Fort(4.32kmph)3) Red Fort to FourFour way( Ram Prasad Mukjarjee
Marg)(4.35kmph)
4) Four - way(Shyam Prasad Mukherjee Marg) toGPO(4.22kmph)
5) GTB nagar to Three - way(Mahatma Gandhi Marg)(4
3.20kmph to
36.09kmph
-
8/13/2019 Time Series Analysis Brt Delhi
48/81
.41kmph)
6) Camp Chock to T.B Hospital(3.49kmph)7) T.B Hospital to Gandhi Ashram(5.17kmph)8) Four - way(outer ring road) to Transport
Authority(3.81kmph)
9) Nathupora(0kmph)3pm-4pm 1) New Delhi Railway Station to Four - way(6.93kmph)
2) Hotel Broadway to Darya Ganj(5.48kmph)3) Subhash park to Three - way (4.09kmph)4) Four - way(Shyam Prasad Mukherjee marg) to GPO
(6.72kmph)
5) Four - way(outer ring road) to TransportAuthority(3.32kmph)
6) Nathupora(0kmph)
5.29kmph to
33.47kmph
4pm-5pm 1) Hotel Broadway to Darya Ganj(4.35kmph)2) Darya Ganj to Subhash park(4.68kmph)3) Subhash park to Three - way(4.90kmph)4) Red fort to 4way(shyam Prasad mukherjee
marg)(4.67kmph)
5) Four - way(Shyam Prasad mukherjee marg)toGPO(3.72kmph)
6) Permanand Crossing to Radio crossing(6.88kmph)7) Four - way(outer ring road) to Transport
Authority(3.27kmph)
8) Nathupora(0kmph)
4.70kmph to
36.09kmph
5pm-6pm 1) Regal cinema to Three - way(4.71kmph)2) New Delhi railway station to roundabout(7.50kmph)3) Hotel Broadway to Daryaganj(6.97kmph)4) Darya ganj to Subhash park(5.93kmph)5) Subhash park to Three - way(6.18kmph)6) Red fort to Four - way(4.07kmph)7) Four - way to GPO(3.13kmph)8) Four - way(outer ring road) to Transport
4.37kmph to
33.20kmph
-
8/13/2019 Time Series Analysis Brt Delhi
49/81
Authority(4.43kmph)
6pm-7pm 1) Three - way(Jawahar lal Nehru marg) to Asif alicollege(2.93kmph)
2) Subhash park to Three - way(3.83kmph)3) Four - way(shyam Prasad mukherjee marg) to
GPO(3.85kmph)
4) Four way(outer ring road) to TransportAuthority(4.43kmph)
5) Nathupora(0kmph)
5.26kmph to
34.07kmph
7pm-8pm 1) International Students Hostel to GTB nagar(7.40kmph)2) Four - way(outer ring road) to Transport
Authority(4.47kmph)
3) Bengali Colony to Francis School(5.82kmph)
6.01 kmph to
32.12kmph
-
8/13/2019 Time Series Analysis Brt Delhi
50/81
3.7.4 185 Down:
The route is from Nathupora to Kendriya Terminal (Fig 4). The route is divided into
segments, the segments being separated by Bus Stopping points like Bus stops,
Intersections (Three way/Four way, Roundabout) (Table 11).The Bottlenecks
identified for different time slots and days are summarized in Table 12.
Fig4: 185 Down
Table 11. Segments for Route 185 Down
S.No. Segment Latitude Longitude Segment Latitude Longitud
1 Nathupura 28.76885 77.18087 27 Merging traffic 28.66067 77.2452
2
Amrit vihar
colony 28.76477 77.18364 28 Intersection 28.65108 77.2457
3 Kaushik enclave 28.76090 77.18938 29 Shanti van 28.64967 77.2456
4 Laxmi vihar 28.76000 77.19114 30 Raj ghat 28.64174 77.2469
5 Burari ganj 28.75791 77.19536 31 Intersection 28.64003 77.2469
6 Burari village 28.75522 77.19871 32
DTC ambedkar
terminal 28.63965 77.2438
-
8/13/2019 Time Series Analysis Brt Delhi
51/81
7 Burari xing 28.75441 77.19908 33
Intersection(jawahar
lal nehru marg) 28.64008 77.24113
8
Sarvodaya
vidtalay burari 28.74862 77.19853 34 Delhi gate 28.63998 77.2387
9 Francis school 28.74470 77.19812 35 LNJP hospital 28.64019 77.235
10 Bengali colony 28.73870 77.19756 36
Intersection/Jawaharlal
nehru marg 28.64037 77.23215
11 Jharoda diary 28.73477 77.19763 37
Four way(maharaja
ranjit singh marg) 28.62907 77.2266
12
Transport
authority 28.73319 77.19803 38 Statesman house 28.63030 77.2239
13 Four way 28.72879 77.19812 39
Three way( outer
circle ,barakhamba
road) 28.63102 77.22269
14 Burari crossing 28.72821 77.20028 40
Three way(outer
circle,Kasturba gandhi
marg) 28.63028 77.22139
15
Three way-
intersection 28.72235 77.21614 41
Three way(near
janpath) 28.6299 77.2197
16 Gandhi vihar 28.72150 77.21874 42
Three way(Outer
circle,Sansad marg) 28.63025 77.2181
17
Gopal pur
crossing 28.71920 77.22386 43 Hanuman mandir 28.63003 77.2136
18 Three way 28.70923 77.22732 44
Gurudwara bangla
sahib 28.62830 77.2097
19 PWD office 28.70264 77.22773 45 St.columbus school 28.62739 77.2076
20 Majni ka tilla 28.69767 77.22732 46 Kendriya terminal 28.62358 77.2064
21 Three way 28.69682 77.22729 47 Kendriya terminal 28.62172 77.2063
22 Matkaf house 28.68344 77.22920 48 Roundabout 28.62091 77.20639
23 Three way 28.67991 77.22937 49 Kendriya terminal 28.61855 77.2060
24 ISBT 28.67118 77.23141 50 Roundabout 28.61772 77.20604
25 Yamuna bazaar 28.66481 77.23538 51 Kendriya terminal bs 28.61734 77.2044
26 Three way 28.66215 77.23935 52 Kendriya terminal 28.61738 77.2037
-
8/13/2019 Time Series Analysis Brt Delhi
52/81
Table 12. Conclusions about 185 Down:
Slot/Day: Bottleneck: Mean speed:
Sundays 1) Transport Authority to Four way( 5.36kmph) 10.08kmph to
36.87kmph
Mondays 1) Nathupora to Amrit vihar colony(1.01kmph)
2) Kaushik enclave to Laxmi vihar(5.09kmph)
3) Transport Authority to Four way(4.10kmph)
4) Intersection(Jawaharlal Nehru marg) to Delhi
Gate(4.90kmph)
8.6kmph to 36.00
kmph
Tuesdays 1) Nathupura(3.10kmph)2) Four way after transport authority to Burari
crossing(4.60kmph)
3) ISBT to Yamuna bazaar(4.02kmph)4) Intersection(Jawahar lal Nehru marg) to Delhi
gate(3.50kmph)
5) Three way(outer circle,barakhamba road) to Threeway(Kasturba Gandhi marg)(3.04kmph)
6) St. Columbus school to Kendriyaterminal(3.70kmph)
8.82 kmph to
39.80kmph
Wednesdays 1) Nathupora to Amrit Vihar colony(5.5kmph)2) Transport authority to Four way(4.87kmph)3) Intersection( Jawaharlal Nehru marg)to Delhi
Gate(4.96kmph)
9.20kmph to
41.20kmph
Thursdays 1) Transport authority to 4 way(3.77kmph)2) Intersection( Jawahar lal Nehru marg) to Delhi gate(
4.67kmph)
3) Kendriya terminal (0 kmph)
7.23kmph to
37.76kmph
Fridays 1) Transport Aurhority to Four way(4.09kmph)2) Intersection( Jawahar lal Nehru marg) to Delhi
gate(5.14kmph)
3)
Kendriya Terminal(4.43kmph)
8.82kmph to
39.06kmph
-
8/13/2019 Time Series Analysis Brt Delhi
53/81
Saturdays 1) Nathupura to Amrit Vihar colony(4.80kmph)2) Transport Authority to Four way(4.05kmph)3) Statesman house to Three way(outer
circle,barakhamba road)(3.90kmph)
4) Kendriya Terminal(3.40kmph)
8.8kmph to 36.90kmph
8am-9am Nathupura to Amrit Vihar colony(3.10kmph) 5.48kmph to
27.22kmph
9am-10am Intersection after DTC ambedkar terminal to Delhi Gate
(5.94kmph)
10.72kmph to
34.28kmph
10am-11am 1) Transport Authority to Three way(4.02kmph)2) Intersection after DTC Ambedkar nagar Terminal to
LNJP hospital
9.03kmph to
37.30kmph
11am-
12noon
1) Transport Authority to Four way(4.05kmph)2) Intersection after DTC ambedkar terminal to Delhi
Gate(4.90kmph)
7.25 kmph to
35.50kmph
12noon-
1pm
1) Kaushik enclave to Laxmi vihar(5.09kmph)2) Transport Authority to Four way(3.07kmph)3) ISBT to Yamuna Bazaar(3.65kmph)4) Intersection after DTC Ambedkar nagar Terminal to
Delhi Gate(4.9kmph)
5) Three way(outer circle,barakhamba road) to Threeway(outer circle,Kasturba Gandhi marg)(5.33kmph)
6) Kedriya Terminal (6.00kmph)
8.87kmph to
36.90kmph
1pm-2pm 1) Transport Authority to Four way( 5.47kmph)2) Intersection after DTC Ambedkar Terminal to Delhi
Gate(5.47kmph)
9.26kmph to
37.07kmph
2pm-3pm 1) Transport Authority to Four way(6.24kmph)2) Intersection after DTC Ambedkar terminal to Delhi
Gate(5.22kmph)
3) Three way(outer circle,barakhamba road) to Threeway(outer circle,kasturba Gandhi marg)(2.33kmph)
6.16kmph to
35.50kmph
3pm-4pm 1) Nathupura to Amrit Vihar colony(1.01kmph)2) ISBT to Yamuna bazaar(3.38kmph) 8.44kmph to
-
8/13/2019 Time Series Analysis Brt Delhi
54/81
3) Delhi Gate to LNJP hospital(5.37kmph)4) Three way(outer circle,barakhamba road) to Three
way(outer circle,kasturba Gandhi marg)
5) St.Columbus school to KendriyaTerminal(3.72kmph)
40.58kmph
4pm-5pm 1) Nathupura to Amrit Vihar colony(4.8kmph)2) ISBT to Yamuna Bazaar(4.02kmph)3) Intersection after DTC Ambedkar Terminal to Delhi
Gate(4.50kmph)
4) Three way( outer circle,barakhamba road to Threeway,outer circle,kasturba Gandhi marg)
5) Kendriya Terminal bs to KendriyaTerminal(5.01kmph)
9.3kmph to 41.20kmph
5pm-6pm 1) Statesman house to Three way(outercircle,barakhamba road)(4.60kmph)
2) Kendriya Terminal(5.12kmph)9.35kmph to
34.09kmph
6pm-7pm 1) Intersection after DTC Ambedkar terminal to DelhiGate(5.14kmph)
2) Kendriya terminal(3.40kmph)8.77kmph to
32.60kmph
7pm-8pm 1) Intersection after DTC Ambedkar terminal to DelhiGate(3.56kmph)
2) Statesman House to Three way(outercircle,Barakhamba road)(3.91kmph)
10.76kmph to
39.06kmph
-
8/13/2019 Time Series Analysis Brt Delhi
55/81
3.7.5 Route: 411 Up
The route is from Nityanand Marg to Ambedkar Terminal(Fig 5). The route is
divided into segments, the segments being separated by Bus Stopping points like
Bus stops, Intersections (Three way/Four way, Roundabout)(Table 13).The
Bottlenecks identified for different time slots and days are summarized in Table 14.
Fig 5: Route 411 up
-
8/13/2019 Time Series Analysis Brt Delhi
56/81
Table 13. List of Bus-stops/Traffic Lights used for segmentation:
Bus Stop/Traffic
Light Latitude Longitude BS/TL Latitude Longitude
1 Nityanand marg 28.66843 77.22476 35 Sri niwaspura 28.56672 77.25294
2 ISBT 28.66873 77.22657 36
Lajpat nagar
crossing 28.56451 77.25026
3 Kashmiri gate 28.66945 77.22824 37 Garhi village 28.56231 77.2515
4
Maharana pratab
isbt 28.66921 77.22883 38
B block east
kailash 28.56205 77.25565
5 GCS university 28.66546 77.23025 39
C block east
kailash 28.56204 77.25834
6 GPO 28.66190 77.23503 40 SNP depot 28.56122 77.26014
7 Four way 28.66031 77.23616 41 Modi mill 28.55672 77.26438
8 Red fort 28.65815 77.23694 42 Three way 28.55543 77.26549
9 Jama masjid 28.65036 77.23875 43
Modi mill
crossing 28.55615 77.26754
10 Subhash park 28.64902 77.23956 44 Modi mill 28.55534 77.26675
11 Darya ganj 28.64570 77.24040 45 Modi mill 28.55435 77.26541
12 Delhi gate 28.64093 77.24095 46 NSIC 28.55220 77.26446
13
Ambedkar
stadium terminal 28.63977 77.24367 47 Three way 28.54931 77.26257
14 Three way 28.63960 77.24649 48 Kalkaji mandir 28.54811 77.26289
15 IG stadium 28.63296 77.24712 49
Govind puri
metro station 28.54520 77.26415
16 IP power station 28.62458 77.24734 50 Kalkaji depot 28.53827 77.26688
17 IP depot 28.61973 77.24891 51 C lal chock 28.53425 77.26851
18
Pragati power
station 28.61590 77.25008 52 Intersection 28.52929 77.27123
19 Three way 28.61325 77.25096 53
Hilgiri
apartments 28.52859 77.27041
20 Pragati maidan 28.61141 77.24606 54
Tughlaqabad
extension 28.52550 77.26079
-
8/13/2019 Time Series Analysis Brt Delhi
57/81
21 Three way 28.61279 77.24032 55 Three way 28.52635 77.25747
22 National stadium 28.61058 77.24017 56 Tara apartments 28.52425 77.25623
23 Zoo 28.60858 77.24012 57 Tughlaqabad 28.52386 77.25610
24 Intersection 28.60735 77.24017 58Guru ravidasashram 28.51984 77.25456
25 Intersection 28.60585 77.24029 59
Guru ravidas
mandir 28.51947 77.25439
26 Sundernagar 28.60216 77.24056 60 Hamdard nagar 28.51721 77.25357
27
Delhi public
school 28.59872 77.24081 61
Apollo-
pharmacy 28.51566 77.25302
28 Roundabout 28.59361 77.24347 62 Three way 28.51201 77.25195
29
Dargah hazrat
nizammudins 28.59139 77.24501 63 Hamdard nagar 28.51193 77.24962
30
Hazrat
nizammudins 28.5896 77.24628 64
Satyanarayan
mandir 28.51222 77.24305
31 Bhogal 28.58296 77.25135 65 Vayusena bad 28.51278 77.23899
32 Ashram 28.57449 77.25683 66 Tigri 28.51291 77.23841
33 Four way 28.57290 77.25808 67 Devli crossing 28.51376 77.23485
34 Nehru nagar 28.56864 77.25413 68
Ambedkarnagar
terminal 28.51507 77.22894
Table 14. Conclusions for 411 Up.
Slot/Day: Bottleneck: Mean speed:
9am-10am
-
8/13/2019 Time Series Analysis Brt Delhi
58/81
university bus station
3) Four way to Redfort4) Subhash park to Darya ganj5) Three way to National Stadium6) Ambedkarnagar terminal
11am-
12noon
-
8/13/2019 Time Series Analysis Brt Delhi
59/81
6) Ambedkarnagar terminal5pm-6pm
-
8/13/2019 Time Series Analysis Brt Delhi
60/81
3.7.6 Route: 411 down
The route is from Ambedkarnagar terminal to Nityanand Marg(Fig 6). The route is
divided into segments, the segments being separated by Bus Stopping points like
Bus stops, Intersections (Three way/Four way, Roundabout)(Table 15).The
Bottlenecks identified for different time slots and days are summarized in Table 16.
Fig 6: Route 411 Down
-
8/13/2019 Time Series Analysis Brt Delhi
61/81
Table 15. List of Bus-stops/Traffic Lights used for segmentation:
Bus
stop/Traffic
Light
Latitude Longitude Bus Stop/Traffic
Light
latitude Longitude
1 Ambedkar
nagar terminal
28.51568 77.22654 35 Nehru nagar 28.56897 77.25382
2 RPS Colony 28.51522 77.22873 36 Four way 28.57220 77.25770
3 Devli crossing 28.51410 77.23375 37 Ashram 28.57371 77.25729
4 Vayusena bad 28.51394 77.23458 38 Bhogal 28.58191 77.25166
5 Tigri 28.51291 77.23890 39 Jangpura 28.58477 77.24957
6 Sri
satyanarayan
mandir
28.51234 77.24317 40 Hazrat nizammudin 28.58932 77.24631
7 Sangam vihar 28.51206 77.24850 41 Dargah hazrat
nizammundin
28.59169 77.24462
8 Hamdard nagar 28.51205 77.24939 42 Roundabout 28.59273 77.24379
9 Three way 28.51193 77.25141 43