Estimating modal split using mobile phone location data: A casestudy of Bamako
Shagun Mittala, Satish V. Ukkusuria*, Fatima Arroyo ArroyobaLyles School of Civil Engineering, Purdue University, 550 W Stadium Ave, West Lafayette, IN - 47907,
USA
mittal53@purdue.edu, sukkusur@purdue.edub
The World Bank, Washington, DC - 20433, USA
farroyoarroyo@worldbank.org* Corresponding author

Extended abstract submitted for presentation at the 11th Triennial Symposium on
Transportation Analysis conference (TRISTAN XI)
June 19-25, 2022, Mauritius IslandApril 4, 22
Keywords: Modal split, K-Means clustering, Data deprivation, Mobile phone location data

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Shagun Mittal, Satish V. Ukkusuri, Fatima Arroyo Arroyo

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1. INTRODUCTION
Transportation analysis typically is performed using surveys especially in the lower income
countries which are infrequently conducted. With the boom in use of mobile phones, a new dataset
collecting the location of individuals has emerged that can be useful in inferring varioustransportation aspects. An important transportation feature is the mode of travel. It is selected bypeople and can assist in comprehending various insights into the characteristics of a region andpeople’s decisions when traveling from a particular region. This can be used to analyze and plan forvarious aspects including connectivity of transit services, availability of nearby opportunities,
quality of roads, walking infrastructure etc. The statistics on this mode of travel are either very
infrequently collected (once in a decade) or reported at a very aggregated scale (overall city). Mobile
phone location data collects precise GPS location and timestamp of anonymous users. This enables
us to the extract user trips from one disaggregated administrative boundary to other and therefore,
analyze the use of modes at such spatial resolutions. A few works have used mobile phone data toinfer mode of travel for a trip. But to our knowledge, this has not been performed for a low-income
nation where both the availability and quality of datasets is low. Such data sparse conditions requirevarious modifications to the typically implemented algorithms in order to obtain the most accurate
results. We conduct this analysis for Bamako, the capital of Mali. This work demonstrates how thealgorithms can be modified to perform modal split with satisfactory results. In this extended abstract,
we describe our methodology used for assessing modal split, the results obtained and a few policybased practical applications.

2. METHODOLOGY
We extract the trips using a modified version of methodology described in Batran et al. 2018. The
information on trip speed, distance, start and end time, and origin and destination coordinates wereextracted. We categorize these extracted trips into walking, transit and driving mode of travel using
the following steps.
Data cleaning: Trips with speeds higher than 180 Km/h were considered a nuisance andconsequently, filtered out. These constituted a very small fraction (<0.1%) of all extracted trips.
Walking trips: The trips with speed lower than 5 Km/h and with distance of travel shorter than 5
Km were considered as walking trips in compliance with the literature (Batran et al. 2018).
Driving and transit trips: For every OD pair, we categorize the trips in between into driving andtransit trips. This is accomplished by initially cleaning the trip data and then performing clusteringto classify trips into driving or transit.
Data cleaning: The trips with speed less than 5 Km/h were screened. These trips would generallyrepresent either walking trips or trips where the user location information is collected a while backbefore the departure from origin or considerably later after arrival at the destination. Moreover, trips
with speed greater than 120 Km/h (maximum speed limit in Mali) or twice the free flow speeds werefiltered out. Thereafter, we filter out origin-destination (OD) pairs that have only one trip among
them, as minimum 2 trips are required to categorize trips into driving and transit.
Classification: Considering all trips between a particular OD pair, we perform K-means (Chen et al.
2016) clustering on speeds to classify transit and driving trips. We observe a high percentage ofoutliers with driving trips. An outlier is defined as the driving trip speed being both less than 0.4
times the free flow speeds and less than 30.5 Km/h (top 10 percentile transit speed). We perform
this classification with multiple thresholds on the minimum number of trips between an OD pair andseek to minimize the overall percent outliers. Figure 1a shows the selection of threshold byminimizing the percent outliers by utilizing heuristic elbow method. Thus, we select a threshold of
3 for the number of trips between an OD pair and screen out accordingly. Figure 1b shows thedriving and transit trip classification post-clustering. The driving trip cluster speeds and free flow
speeds fit well with a statistically significant slope of 0.83 (p<0.01). However, certain outliers stillpersist. In general, the speeds obtained using mobile phone data are underestimated for both transitand driving.

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Shagun Mittal, Satish V. Ukkusuri, Fatima Arroyo Arroyo

3Figure 1: (a) Selecting threshold on number of trips between an OD pair. Following the heuristic elbow method, athreshold of 3 was selected. (b) Driving and transit trips after performing K-means clustering where each marker
represents the average driving (or transit) speeds for each OD pair for the classified trips. The best fit line shows thatdriving cluster speeds, obtained from mobile phone data, are around 0.83 times the free flow speeds. The red coloredmarkers depict an outlier speed for driving as mentioned in earlier. These outliers persist even after the use of the thresholdand were eventually eliminated from the data.

3. RESULTS

Figure 2: Distribution of proportion of trips generated in Bamako city corresponding to driving, transit and walking
modes of travel. Overall the proportion of walking trips are higher in magnitude when compared with transit and driving.
The proportion of driving trips is highest in outer Greater Bamako regions.

The distribution of modal split for each quartier located in the city of Bamako is depicted in figure
2. Modal split is the proportion of trips generated from a zone via one particular mode of travel. Weobserve that from the outer regions (especially in Rive Droite areas), the proportion of driving andtransit trips is higher than that of walking trips. However, in almost all other regions, walking tends
to be the major mode of travel. Overall the median percentage of walking trips from the regions
were 49%, i.e., for the majority of regions the percentage of trips by walk from the region is greaterthan 49%. The median percentage of driving trips from the regions is 22% while for transit it is 28%.
A greater proportion of walking trips implies larger travel times to access facilities. Lower
proportion of driving trips can be attributed towards high costs of owning a vehicle; however, lower
proportion of transit trips are due to the inadequacy of the transit system in terms of optimallyconnecting various regions, higher speeds and timely service.

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Shagun Mittal, Satish V. Ukkusuri, Fatima Arroyo Arroyo

4These proportions of modal split are comparable to the results from earlier reports using surveys,
where 57% of trips were observed via walking and about 24% via driving. The comparisons are at
the overall city scale resolution due to the unavailability of data at a more disaggregate resolution.
Table 1 compares the two results.
Table 1: Comparison of modal split using mobile phone data with an earlier report (Olvera et al. 2012)

Mobile Phone Data - Median Modal split
percentage
Report on Human Settlements (2013) [4] Modal Split Percentage

DrivingTransit

Walking22%

28%

49%

24%

17%

57%

4. Discussion
This section presents a few policy based implications of the analysis performed.
Transit is critical for a growing economy like Bamako. It can act as an affordable option for thepopulation unable to buy a vehicle and can be critical to diminish congestion. Understanding the trip
generation and attraction trends of transit trips is important to realize the current movement
dynamics and to infer regions where improvements are necessary. We observe that during morning
peak hours, trips are considerably attracted by regions located in central parts of Rive Gauche area,
whereas during evening peak hours, the same regions generate a high number of trips (figure 3). The
transit trip generation and attraction distributions during morning and evening peak hours
complement one another, indicating work-based trips. Overall, the 25% poorest regions generate
and attract low proportions of transit trips in comparison to all zones. On a weekday, about 1.3%

Figure 3: Transit trip generation and attraction for (a-b) morning peak hours (7-10 AM), and (c-d) evening peak hours(4-7 PM). Regions with yellow boundary have least trip generation / attraction while regions with cyan colored boundary
have highest trip generation / attraction. Morning and evening peak hours’ trends complement each other.

transit trips occur from these poorest to the 25% richest regions while around 0.05% transit trips
occur from poorest to poorest regions. While only about 5.5% transit trips are attracted by thesepoorest regions from the richest ones. Throughout the day, about 18% transit trips occur from rich

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Shagun Mittal, Satish V. Ukkusuri, Fatima Arroyo Arroyo

5to rich regions possibly due to more transit stop density in richer zones. People travel from poorer
to rich regions to access their jobs but not many people travel from richer to poor regions. Therefore,
poorer regions clearly lack in both the job opportunities and the transit facilities. With poorer people
not owning vehicles and relying more on transit for longer travels, transit facility improvements aremust.
Moreover, we also observe that the poorer regions have higher potential of generating transit trips
in comparison to the richer regions. Transit potential is defined as the transit trips generated per user
divided by the transit stop density. Intuitively, it is the transit trips generated per transit stop, scaledup to the population of the zone (in comparison to the number of users extracted by the mobile phonedata). The figure 4 shows that the transit potential for poorer regions is higher in Bamako. The transit
stops located in poorer regions execute a higher number of trips per transit stop when compared to
the richer zones. Observing the relation between poverty and transit potential, we highlight a fewregions which are extremely poor, have high transit potential and therefore are good candidates fortransit improvements (figure 4b). Improving transit in these regions is essential as people from theseregions might not be able to afford personal vehicles. Upgrade in transit can enhance the access ofthese regions leading to their economic growth overall.

Figure 4: Relation of transit potential and poverty for the Bamako city. Transit potential is higher in regions with higher
poverty. The regions with high poverty and high transit potential should be targeted for transit improvement.

In this study, employing passively collected mobile phone data we inferred the modal split of eachquartier within Bamako using a modified methodology. The modal split validated well with theprevious reported statistics using surveys. From majority of regions, a higher proportion of walking
trips is observed when compared to transit and driving, the result urging for improvements inwalking infrastructure within the city. We also extended the results to evaluate particularly the transit
trips. We observed that very few transit trips are being generated and attracted by the poorer regions.
Moreover, the poorer regions have consistently higher trips generated per transit stop whencompared to the richer regions, normalizing for the population. These policy-based extensions show
the need to invest in transit particularly in the poorer regions. Variations in the representativeness ofpopulation exist within the city, however we normalize the extracted trips by the number of users
extracted. In addition, concerns like multiple devices carried by a person, and bias in market share
have been noted in earlier studies and may induce potential bias. Studies like this are important forunderstanding the current and monitoring the changes in mobility of people at an extremely
disaggregated level.

References
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