An Approach to Evaluate the Impact on Travel Time of Bus Network

Changes

Kathrin Rodríguez Llanes

, Marco A. Casanova

, Hélio Lopes

and José Antonio F. de Macedo

Department of Informatics, Pontifical Catholic University of Rio de Janeiro, RJ, Brazil

Department of Computing, Federal University of Ceará, CE, Brazil

Keywords: Travel Time Estimation, Trajectory Data Mining, Bus Network.

Abstract: This paper proposes an approach to evaluate the impact of bus network changes on bus travel time. The

approach relies on data obtained from buses equipped with GPS devices, which act as mobile traffic sensors.

It involves three main steps: (1) analysis of the bus network to determine which road segments are frequently

traversed by buses; (2) computation of bus travel time patterns by segment; (3) evaluation of how much the

bus travel time patterns vary when bus network changes take place. The approach combines graph algorithms

and geospatial data mining techniques. It can be applied to cities served by a dense bus network, where buses

are equipped with active GPS devices that continuously transmit their position. The paper applies the proposed

approach to evaluate how bus travel time patterns in the City of Rio de Janeiro were affected by traffic changes

implemented mostly for the Rio 2016 Olympic Games.

1 INTRODUCTION

Public transportation affects people in their daily

routine and, therefore, must be efficiently

implemented. Bus networks are among the most

popular public transportation systems, but obviously

have a strong interdependency with traffic conditions

and, therefore, may result in a considerable waste of

time by quite a large number of citizens.

To improve traffic conditions, reduce travel

times, avoid traffic congestion and reduce conflicts

between the bus network and other means of

transportation, city authorities continuously monitor

and revise the transportation policies and the road

network. Policies include exclusive bus lanes (Lindau

et al. 2014), bus lane combinations, traffic signal

priority for buses, street-running light rail systems

(Feitelson & Rotem-Mindali 2015), and Bus Rapid

Transit (BRT) routes (Deng & Nelson 2013), among

others. These strategies improve passenger comfort

and the public transportation service quality.

Once such adjustments are implemented, it

becomes essential to measure how effective they are

(Carrigan et al. 2013), and, if necessary, plan

alternative action to mitigate problems. In this sense,

bus travel time information is an important indicator

for assessing the bus network efficiency.

The specific problem addressed in this paper is

how to quantitatively evaluate the impact of bus

network changes on bus travel time. We note at this

point that we treat a road network change that affects

bus routes as a bus network change.

To face this problem, we propose an approach that

considers buses equipped with GPS devices as mobile

traffic sensors and estimates the travel times based on

bus trajectory data generated by the GPS devices. The

approach combines graph algorithms and geospatial

data mining techniques. It involves three main steps:

(1) analysis of the bus network to determine which

road segments are frequently traversed by buses; (2)

computation of bus travel time patterns by segment;

(3) evaluation of how much the bus travel time

patterns vary when bus network changes take place.

The paper has two primary contributions: (1) an

approach to evaluate the impact of bus network

changes on bus travel time, based on bus GPS raw

trajectories; and (2) an evaluation of how bus travel

time patterns in the City of Rio de Janeiro were

affected by traffic changes implemented mostly for

the Rio 2016 Olympic Games.

The remainder of this paper is organized as

follows. Section 2 introduces the main concepts used

and formalizes the problem. Section 3 gives an

overview of the proposed approach. Section 4

describes the steps to select the paths whose bus traffic

Llanes, K., Casanova, M., Lopes, H. and Macedo, J.

An Approach to Evaluate the Impact on Travel Time of Bus Network Changes.

DOI: 10.5220/0006231700230032

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 23-32

ISBN: 978-989-758-247-9

is dense enough to be monitored with the help of bus

trajectories. Section 5 presents the steps to discover

bus traffic patterns. Section 6 describes the

experiments with real data and discusses the results.

Section 7 discusses related work. Finally, Section 8

concludes the paper.

2 BASIC CONCEPTS

A bus network is a labelled, directed graph

  



 



 



 



 



 



, where

– 



associates a geo-referenced point (in an

appropriate geographic coordinate system) with

each node in 



– 



associates a geo-referenced line string (in the

same geographic coordinate system) with each

edge in 



– 



labels each node in 



with the bus routes

that pass through 

– 



labels each edge in 



with the bus routes

that pass through 

Intuitively, the edges represent road segments that

buses traverse and the nodes indicate the start and end

points of such road segments.

A bus network version is a triple



  



 





where  is a bus network and 



and 



are

timestamps that delimit the period   







during which the bus network maintained the same

characteristics (such as structural features and the bus

routes).

A monitored bus network is a subgraph of

Intuitively, a monitored bus network consists of the

nodes and edges of  that are frequently traversed by

buses so that meaningful statistics can be computed.

A monitored path is a path 



of B. The control

points pair of 



is the pair 





 





, where 





is the

start node and 





is the end node of 



. Note that 



provides a geo-referencing for the control points and





provides a geo-referencing for the path.

A raw bus trajectory s is a sequence

  



 



 



 



     



 



 such that





 



 



 is a geo-referenced point and 



is a

timestamp such that 



 



, for     . A raw

bus trajectory s represents the position evolution of a

moving bus.

A travel time pattern for a monitored path over a

period of time is any statistical measure of the travel

time of the buses that traverse the given path during

the given period, represented by a function.

Given a monitored bus network

, the travel time

pattern problem for

refers to the problem of

determining bus travel time patterns for a given set of

monitored paths of

over a given period. Given two

monitored bus network versions,

and

, the

problem of travel time pattern deviation refers to

problem of determining how much does the travel

time patterns deviate from

, for a given set of

pairs









 







of monitored paths, where 





is a

monitored path belonging to

=1,2, at a given

period. Note that the monitored paths may not be the

same in both versions, since one may wish to compare

alternative bus routes in the two versions.

3 OVERVIEW

The approach we propose to evaluate the impact of

bus network changes on bus travel time depends on

data generated by GPS devices installed in buses. In

that sense, buses equipped with GPS devices are

treated as mobile traffic sensors, which describe

trajectories that cover the same set of streets, at

predictable regular intervals. Therefore, our approach

can be applied to cities served by a dense network of

buses, equipped with GPS devices, that continuously

transmit their position.

Figure 1 summarizes the proposed approach. As

illustrated, the approach involves three main stages:

(1) definition and segmentation of monitored bus

network; (2) discovery of travel time patterns for each

monitored path; and (3) evaluation of how much

travel time patterns vary when bus network changes

take place. It combines graph algorithms and

geospatial data mining techniques.

To evaluate how much the travel time patterns

vary when the bus network changes, two different

versions of the bus network must be analysed. One

comprises the bus network features corresponding to

the period before the changes, and the other one to the

period after the changes. Our approach will then

receive as input two different bus networks and

historical bus GPS trajectory data (depicted by light

grey boxes in Figure 1). Then, both networks are

processed independently in stage 1 and 2. As a result,

their travel time patterns are obtained. The travel time

patterns of both networks are compared in Stage 3.

We observe that changes on the road network may

also produce changes on the bus network, from

alterations in the direction of traffic flow to the

construction of new road segments. In that sense,

maintaining different versions of the bus network, the

monitored bus network and the traffic pattern

supports comparing them to assess the impact of

changes on select street segments, which provide a

useful tool for city planners.

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Figure 1: Stages of the approach to evaluate the impact on travel time of bus network changes.

Furthermore, to compare different versions of

travel time patterns, given two functions representing

the travel time patterns before and after any changes

in the bus network, we estimate the variation on travel

time between them by computing the area of the

region between the function values.

Lastly, we observe that some changes in the road

network may not produce structural changes in the

bus network, but produce changes in traffic patterns.

Examples are the introduction of preferential bus

lanes and the construction of new road lanes. In such

cases, Stage 1 is computed only once for both

network versions.

4 MONITORED NETWORK

In this section, we present the algorithms for selecting

the road segments whose traffic will be monitored

with the help of bus trajectories.

Specifically, Algorithm 1 computes the monitored

bus network, Algorithm 2 selects candidates for

monitored paths, and Algorithm 3 refines the

candidate monitored paths points.

4.1 Computation of the Monitored Bus

Network

We recall that, intuitively, the monitored bus network

is the set of the road segments most traversed by

buses. Algorithm 1 computes the monitored bus

network as follows.

Select the Most Traversed Road Segments. The

algorithm receives as input the bus network. Line 2

ranks the edges by the number of bus routes that

traverse them and returns the most traversed edges.

Find Connected Components. For each edge in the

set of the most traversed edges, Lines 5 and 6

compute the initial and final nodes of the edge, and

Line 9 performs a reverse breadth-first search (BFS)

over the bus network starting from the initial node of

the edge. Line 10 executes a direct BFS starting from

the final node of the edge. Both modifications of BFS

algorithm (reverse BFS and direct BFS) explore the

neighbour edges first, before moving to the next level

neighbours and they are including in the result set the

edges that are served by the same set of bus routes

that serve the most traversed edge under analysis.

When an edge served by a different set of bus routes

is encountered, the algorithms stop. Thus, the

algorithms form sub-paths composed by connected

edges that are served by the same bus routes. As a

Bus

Network

Segmentation

Monitored Bus Network B

Bus

Network

Definition

An Approach to Evaluate the Impact on Travel Time of Bus Network Changes

Algorithm 1: Computation of the Monitored Road Network.

result of both searches, two sub-paths are obtained.

Line 11 combines both sub-paths and the edge under

analysis to compose a subgraph. As new edges are

founded by the direct and reverse BFS, they are

removed from bus network and from the list of most

traversed edges to avoid infinite loops. Lines 12 to 19

then gradually reduce the bus network and the list of

the most traversed edges until they are empty. Line

20 adds each subgraph, generated by each of the most

traversed edge, to a set of subgraphs. The same

process (Line 4 - 20) is repeated until all edges in the

most traversed set are analysed. Line 22 calls a

function to find, within the set of subgraphs, those

that have a common node and joins them in a single

connected component. Thus, a set of disjoint

subgraphs is obtained, which is the monitored road

network. Finally, Line 23 returns the monitored road

network, represented by its connected components.

4.2 Segmentation

To segment the monitored bus network, we use the

concept of control points. Then, monitored paths

composed by a sequence of connected road segments

are obtained, which are the minimal unit for

monitoring the behaviour of buses.

Algorithm 2: Computation of the candidate control points.

Algorithm 2 determines control points in the

monitored bus network as follows.

Cluster Edges by Bus Routes. The algorithm

receives as input the set of connected components that

form the monitored bus network. Line 4 applies a

clustering function to each connected component that

groups edges traversed by the same bus routes.

Find Disjoint Paths between the Same Cluster.

Segments that correspond to the same cluster may be

consecutive or not. If they are consecutive, they form

longer paths served by the same bus routes. Line 6

combines all such paths into the same group.

Determine the Initial and Final Nodes of Disjoint

Paths. Lines 8-9 compute the initial and final nodes.

For each path in the set disjPaths. Line 10 adds these

pairs of initial and final nodes to the list of candidate

control points and the monitored path between them.

Finally, Line 14 returns a list of candidate control

points.

However, not all control point candidates have the

same level of relevance in terms of traffic monitoring.

For instance, one may discard intermediate nodes

connecting two consecutive paths of the monitored

road network that belong to the same street. In such

cases, there is no significant difference in the bus

routes serving each path. For this reason, both paths

can be combined.

To address this issue and improve the quality of

the segmentation process of the monitored bus

network, Algorithm 3 refines the set of candidates for

control points, using data provided by the road

network map, as follows.

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Algorithm 3: Refine the list of candidates for control points.

Find Intermediate Nodes. Line 2 assigns to the

ctrlPts variable the list of candidate control points,

passed as input. Line 3 computes the intermediate

nodes between the list of candidates for control

points. Given two pairs of candidate control points,

an intermediate node is a node that is the end node of

one of the control points and the initial node on the

other.

Discard Non-relevant Intermediate Nodes. For

each node in the intermediate node list, Lines 5 and 6

extract, according to the direction and sense of the

street, its previous and subsequent nodes in the

monitored bus network. Neither the previous node,

nor the subsequent node must necessarily be

candidates for control points. It occurs just when the

path delimited by a pair of control points, where one

of the points is an intermediate node, encompasses

only one street segment.

Using data from the network map, a plausible

name of the street that connects the previous node

with the intermediate node can be obtained, as well as

the name of the street that connects the intermediate

node with the subsequent node. For this purpose, Line

7 and 8 call a crawler-function that processes

machine-readable road tags and the semantic

relations between them, to extract the name of the

street in question, to which two given coordinate

points belong.

Once the street names are found, Line 9

compares them. If the names are the same, both street

segments belong to the same street, and it means that

there is no change of street around the intermediate

node. Therefore, both paths, where the intermediate

node belongs can be joined into one to be monitored.

Line 10 removes the intermediate node from the list

of candidate control points. This process is repeated

until all nodes of the intermediate nodes list have been

analysed and the list of control points has been fully

Algorithm 4: Estimation of travel time.

refined. Line 12 returns the list of control points as

output.

5 TRAVEL TIME PATTERNS

Once the Monitored Bus Network is defined and

segmented, it is possible to mine the historical bus

GPS trajectory dataset to perform the following

operations: (1) estimation of the travel time that buses

take to traverse a given path, delimited by a pair of

control points, at a given time interval; and (2)

computation of the travel time patterns for a given

path at a given repeating time interval (for example,

every weekend). In this section, we implemented two

algorithms to execute these operations. The

algorithms consider that the period corresponds to a

day, divided into intervals (i.e. 24 fixed time intervals

of 1 hour each). Also, the second algorithm computes

only the average travel time, and not a generic time

travel statistics. However, we note that both

algorithms can be easily modified to account for more

general settings.

5.1 Estimating Travel Time

Algorithm 4 estimates the travel time that buses take

to traverse a monitored path delimited by a pair of

control points at a given time interval as follow.

Buffer Zone Definition: The algorithm receives as

input the monitored paths, and a period covered by

the period of the network. For each monitored path,

Line 3 extracts the LineString that joins the

consecutive geographical positions forming the path.

Line 4 creates a buffer zone around the LineString,

An Approach to Evaluate the Impact on Travel Time of Bus Network Changes

with a specific width. Note that the width value is

computed as the sum of the width of the street under

analysis and the GPS measurement error, which

typically ranges from 5 to 10 meters. As a result, the

buffer zone is a polygon, used to temporarily delimit

the raw bus GPS observations transmitted between a

pair of control points.

Filtering GPS Observations by Buffer Zone (Geo-

spatial Segmentation of Raw Trajectory Data).

Line 5 executes a geospatial-temporal query to

retrieve all GPS observations inside the defined

buffer zone for the specified period. This allows to

select GPS points that may not exactly fit road

geometries, without having to execute (expensive)

map-matching operations.

Travel Time Computation: Line 6 finds all distinct

buses (busLine, busId) that transmitted their positions

within the buffer zone. Line 7 repeats the loop to read

each found bus. Line 8 extracts only the observations

that correspond to trips that go in the direction from

first to second point of the pair of control points. Line

9 computes the trips. For those trips for which the first

or the last observation do not match the position of

the control points, a linear interpolation is used to

discover the timestamps when the bus passed through

the control points. Line 10-13 computes and saves the

travel time for each trip.

It is worth mentioning that Algorithm 4 computes

the travel time of trips made in the period defined by

the input parameter day , whose value may be set to

be one day or multiple days belonging to the period

of each road network stored in the dataset. To make

the computation for many days, one only has to pass

a set of days as value of the input parameter.

5.2 Computing Travel Time Patterns

Algorithm 5 computes the travel time pattern of each

path, at a given time interval. Again, for simplicity, it

considers that the period corresponds to a day,

divided into 24 fixed time intervals of 1 hour each,

but the code can be easily generalized.

The algorithm receives as input a specific day, the

paths of the monitored bus network and the intervals

dividing the day. Line 2 uses a loop to analyse each

of these paths. For each path, Line 3 retrieves the

travel time table that corresponds to the bus trips

made in the specified day. Line 5 is a second loop that

steps through each interval of the day. Line 6 recovers

trips whose time of entry into the path belongs to the

time interval being analysed. Line 7 counts the

number of these trips. The travel time pattern for this

particular interval is computed in Line 8 as the mean

Algorithm 5: Computation of the travel time pattern for all

paths of the Monitored Network during a given day.

travel time for the same path at the referred time

interval. Line 9 saves the travel time pattern for future

analysis. This process is repeated until all intervals of

the day have been examined.

Since Algorithm 5 computes the travel time

patterns of all segments of the monitored bus

network, but only for one day, it must be run for all

days belonging to the period of the road network. As

the volume of data to be processed is very large, to

reduce the execution time, a distributed algorithm has

been designed and implemented. It will not be

explained in this paper due to space limitations.

6 EXPERIMENTS

The experiments evaluated how bus travel time

patterns in the City of Rio de Janeiro were affected by

traffic changes implemented mostly for the Rio 2016

Olympic Games. To support the evaluation, a large

data set containing the GPS positions (more than 3

billion samples) of all buses that operated in the City

of Rio de Janeiro from June 12

, 2014 until

November 30

, 2016 was used. Each sample contains

a timestamp, the bus identifier, the line number, the

position (as latitude and longitude) and the speed.

The traffic changes we evaluated were: the

construction of the New Joá Elevated Road; the

introduction of exclusive bus lanes for bus rapid

service (BRS) on the Voluntários da Pátria and São

Clemente Streets; and the construction of the bus

rapid transit (BRT) corridor of the Americas Avenue.

The New Joá Elevated Road has 5 km of

extension and 2 lanes, whereas the Old Joá Elevated

Road – still in operation – has 4 lanes. They both

connect the south zone of Rio and Barra da Tijuca (a

neighbourhood in the west of Rio where the Olympic

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Games took place). We then have two scenarios,

which we call old and new, defined as follows:

 Old scenario: just the Old Joá Elevated Road,

with 2 traffic lanes in each direction, except

during the morning traffic peak hours, when 3

lanes were used for traffic flowing from Barra da

Tijuca to the south zone;

 New scenario: the Old and New Joá Elevated

Roads, which in combination offer 3 traffic lanes

in each direction, all day long; in each direction,

one of the lanes is reserved for cars. There is no

use of a reverse lane in the morning.

The bus routes connecting the south zone and

Barra da Tijuca greatly benefited from this new traffic

scenario. Our experiments focused on the bus traffic

from Barra da Tijuca to the south zone, with emphasis

on the morning peak hours.

The construction of the New Joá Elevated Road

started at the end of June 2014, and the new road was

inaugurated on May 28

, 2016. In our evaluation, we

considered two periods: from June 12

, 2014 to May

, 2015; and from May 28

, 2016 to November

, 2016. All trajectories in the period from May

, 2015 to May 28

, 2016 – the peak of the

construction of the new road – were eliminated from

the sample to avoid introducing noise in the

computation of travel time.

To execute Stage 2 of the approach (see Figure 1),

we selected a path of the monitored network that goes

from Ministro Ivan Lins Avenue to the Gávea Road

(in the direction from Barra de Tijuca to the south

zone). This path was heavily affected by the

construction of the new elevated road.

For the old scenario, we analysed a total of 24,846

trajectories, generated by 1,011 buses, serving 70

daily routes, that cover the path under study.

Corresponding to the new scenario, we analysed a

total of 8,310 trajectories, generated by 115 buses

serving 66 daily routes.

Since the travel times in weekdays differ

dramatically from weekends, within the same

scenario, we analysed these periods separately.

Figure 2a shows the travel time patterns for the

weekdays belonging to the old scenario (v1) versus

the new scenario (v2), while Figure 2b depicts the

travel time patterns for the weekends.

At Stage 3 of the approach, we computed the area

between the two curves during the morning peak

hours (from 6 to 10 o'clock). The result was 15.00.

This means an average reduction of the travel time in

the morning peak hours by approximately 4 minutes.

As the graphs in Figure 2a corroborate, there are

significant variations in travel time from one pattern

to the other, specifically at the peak hours in the

morning (from 6 to 10 o'clock), when the flow of

vehicles in the direction Barra de Tijuca - south zone

is larger than during the rest of the day. The results of

the experiments then demonstrate that the

commissioning of the New Joá Elevated Road

produced significant a reduction of bus travel time

from Barra da Tijuca to the south zone.

The experiments related to the introduction of the

preferential bus lane on the Voluntários da Pátria and

São Clemente Streets on August 2

, 2014 indicated

that these changes did not produce significant benefits

in terms of the reduction of bus travel time. By

contrast, the BRT corridor on the Americas Avenue,

inaugurated on August 23

, 2016 reduced bus travel

time by 45%. The results of these experiments were

omitted due to space limitations.

7 RELATED WORK

We review work which is closely related to our study,

divided in: (i) segmentation of raw trajectories; (ii)

estimation of traffic patterns from GPS data; and (iii)

traffic impact of road network changes.

Segmentation of Raw Trajectories. There are

different criteria to segment raw trajectories. They

range from the transportation means used (Biljecki et

al. 2013), potential-transition locations (e.g. bus

stops) (Liao et al. 2006), geo-spatiotemporal

information (Buchin et al. 2011), detection of similar

sub-trajectories (Sankararaman et al. 2013) and

movement analysis (Alewijnse et al. 2014). In this

paper, we specifically discuss how to segment row

trajectories based on the passing of buses by control

points.

Extraction of Traffic Patterns from GPS Data.

Multiple traffic patterns can be estimated using

historical GPS trajectory data, such as: traffic flow,

traffic demand and travel time. The extraction of

these traffic patterns pursues two main objectives: to

explain common traffic conditions and to predict

future traffic conditions. Work focused on the first

objective addresses traffic monitoring [Blind1],

detection of traffic anomalies (Kuang et al. 2015),

traffic performance analysis (Shi et al. 2008). Work

focused on the second objective includes traffic state

prediction (Zhang et al. 2013), urban traffic

congestion forecasting (Hou et al. 2012; Kong et al.

2016), prediction of traffic anomaly duration (Li

2015) and estimating time of arrival (Coquita et al.

2015; Kormáksson et al. 2014; Jithendra. H. K 2015).

This paper is targeted at the first objective,

specifically to model past and current traffic

behaviour based on travel time patterns.

An Approach to Evaluate the Impact on Travel Time of Bus Network Changes

Traffic Impact of Bus Network Changes. There

have been a variety of bus priority strategies adopted

to reduce bus travel time delays, increase passenger

comfort and thereby improve public transportation

service quality. Studies assessing the implementation

impacts of some of these strategies analyze: traffic

signal priority for buses (Daniel et al. 2004),

exclusive bus lanes (Chen et al. 2016), bus lane

combinations (Truong et al. 2015; Fowkes et al.

2014), bus rapid transit (BRT) implementation

(Carrigan et al. 2013), bus operation in platoon

(Shrestha et al. 2009). More comprehensive studies

(Bhattacharyya et al. 2016) analyse the effects of

multiple bus priority treatments simultaneously. This

paper focuses on the evaluation of travel time impacts

of various bus network changes implemented in the

City of Rio de Janeiro mostly for the Rio 2016

Olympic Games.

8 CONCLUSIONS

In this paper, we proposed an approach to evaluate the

impact of bus network changes on bus travel time.

The approach relies on data obtained from buses

equipped with GPS devices, which act as mobile

traffic sensors. This type of study allows analyzing

the evolution of the city with respect to urban

mobility in a systematic way, similarly to the analysis

of population growth and occupation of land made

with satellite images. It also provides urban planners

unprecedented opportunities for better understanding

urban transportation system and for better exploiting

the knowledge thereof.

Using the proposed approach, we investigated the

effect on bus travel time of traffic changes

implemented in the City of Rio de Janeiro mostly for

the Rio 2016 Olympic Games.

Directions for future research include the

estimation of the impact of road network changes on

traffic flow. In this context, statistical quality control

to evaluate current conditions with respect to normal

condition based on historical data will be applied. We

are also implementing a traffic observatory that will

help city planner analyze bus network changes and

that will include a real-time component to help alert

city authorities about anomalous traffic conditions.

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An Approach to Evaluate the Impact on Travel Time of Bus Network Changes

Figure 2a: Travel Time Patterns for weekdays of v1 vs v2 Lagoa - Barra Highway.

Figure 2b: Travel Time Patterns for weekends v1 vs v2 Lagoa - Barra Highway.

ICEIS 2017 - 19th International Conference on Enterprise Information Systems