1. 2018 Southern african transport conference
July 2018
Understanding the Operational Characteristics of Paratransit Services in Accra, Ghana
A Case Study

2. Introduction Research carried out in Accra, Ghana
Background
Methodology
Results
Introduction
Research carried out in Accra, Ghana
AccraMobile: a partnership between Accra Metropolitan Assembly, Concordia University, a French NGO, and Transitec, supported by AFD
Started in 2015, now in its third phase
For those of you who are not familiar with West Africa, this is where is Ghana is, right between Côte d’Ivoire and Togo
This project was carried out by the Department of Transport of the municipality of Accra and the TRIP Lab at Concordia University (Montreal, Canada), with funding and facilitation from the French development agency (AFD)
This is the second phase of an initiative called AccraMobile, which aimed at collecting and disseminating data on paratransit in Accra. First phase of this project started in 2015 and was presented here last year.
This research is part of a larger urban transport project co-financed by the WB. The core of that project was the development of a bus corridor in Accra, but there was also an institutional development component, and this second phase is part of it.
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3. Heatmap of stops density
Background
Methodology
Results
Results from Phase 1
In the first phase of the project, we had recorded the entire transport network of the city and created the first map of it (that you can see on the left)
But because there were over 300 routes to record, we could only travel each of them once, so it was not possible to draw any general conclusions on the performance of this or that route…
One thing we did collect was information on all the stops that were made by the vehicles and the number of passengers boarding and alighting at different locations
And so we produced this heatmap (on the right) that shows the areas where the level of activity is the highest.
Some of them are proper terminals, others are just areas where you can find a high number of stations on a limited space
Schematic network map
Heatmap of stops density
Accra’s DoT created in 2015 to regulate passenger transport services
Very limited data available on existing routes and operators
AccraMobile initiative was born to create the first map of the network
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4. Headway variations a weekday Illustration of the trip variation index
Background
Methodology
Results
Results from Phase 2
Headway in min.
Now the third indicator that we looked at is not a typical in the transit quality of service literature.
In fact, one of the specificities of paratransit routes in Accra is that their origin and destination might be fixed, but the itinerary between these two points varies.
To measure how much it changes, we developed an index that we call the Trip Variation Index.
I won’t go into the details of the equation but basically what it does is calculate the proportion of overlap between different trips taken on a given route. The higher it is, the more consistent the itinerary is.
Here you have an illustration of two different cases: low variability on the left, where almost all the trips recorded used the same itinerary, so you get a score of 98%
On the right is an example of route with a lower score, where you can see quite a bit of variation in itinerary in the middle of the route
Overall, routes are fairly consistent, with a mean TVI of 88%, but some routes exhibit significant variations, with less than half of their itinerary stable across trips.
Perhaps the most interesting results though was that 7% of the vehicles never made it to their final destination.
Two mains reasons for that:
mechanical problems (lots of vehicles are old and poorly maintained so they experience frequent breakdowns – in one instance the minibus just ran out of gas)
or the driver decides that it is no longer profitable to work that route to its end (because there are too few passengers left in the car or too much traffic ahead), so he asks the passengers to get off the bus
Raises the question of what is an acceptable level of variability from the user’s perspective? Even if there is not a lot of variation of itinerary on average, imagine how you would feel if every time you use transit, there was one chance out 14 that the bus or the metro will not go all the way to its final destination…
Headway variations a weekday
Phase 2 looked at quality of service from a user perspective
Overall reliable and consistent levels of service on average
Travel time variation ⩽ 10 min on 70% of routes
But 7% of the trips never made it to their final destination!
Illustration of the trip variation index
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5. Methodology 1/ Measure the characteristics of a typical trip
Background
Methodology
Results
Methodology
Scope of the case study: one station serving six different routes (≈ 70 vehicles)
1/ Measure the characteristics of a typical trip
≈ 127 round trips recorded onboard vehicles
2/ Record the number of departures per route
3,526 departures recorded over a full week
3/ Determine operators’ and route performance
By expanding trip characteristics by number of departures
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6. Smartphone-based Data Collection
Background
Methodology
Results
Smartphone-based Data Collection
DataMobile/Itinerum
TapLog
Data collection done exclusively using smartphones
Important gains in efficiency and reliability
Publication of data on OpenStreetMap (+ GTFS)
Limitation of used apps: manual work required to retrieve, clean up, merge, and analyze data
Process can be streamlined using dedicated apps
OSM Tracker
All the data collection was done using smartphones, that had two different apps installed.
The first one is DM, developed by the TRIP Lab at Concordia University
Originally a travel survey app. Used a slightly modified version to essentially record GPS traces passively in the background
Second one is TL: personal event logger, that you custmizte to record different types of variables: text, numbers and GPS coordinates.
Every day, collectors received their assignment via Whatspp, they went to the station to collect data, and sent it by from their phone at the end of the day. Reduces the need for meetings in person and trips to the project office.
The team based in Accra reviewed the data in the evening, and sent to the team in Canada, and taking advantage of the time difference, it could be mapped on the same day of there was no problem.
All in all 1,200 trips were recorded as well as 15,000 departures from different stations.
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7. Passenger Volumes by Section
Background
Methodology
Results
Passenger Volumes by Section
Now the third indicator that we looked at is not a typical in the transit quality of service literature.
In fact, one of the specificities of paratransit routes in Accra is that their origin and destination might be fixed, but the itinerary between these two points varies.
To measure how much it changes, we developed an index that we call the Trip Variation Index.
I won’t go into the details of the equation but basically what it does is calculate the proportion of overlap between different trips taken on a given route. The higher it is, the more consistent the itinerary is.
Here you have an illustration of two different cases: low variability on the left, where almost all the trips recorded used the same itinerary, so you get a score of 98%
On the right is an example of route with a lower score, where you can see quite a bit of variation in itinerary in the middle of the route
Overall, routes are fairly consistent, with a mean TVI of 88%, but some routes exhibit significant variations, with less than half of their itinerary stable across trips.
Perhaps the most interesting results though was that 7% of the vehicles never made it to their final destination.
Two mains reasons for that:
mechanical problems (lots of vehicles are old and poorly maintained so they experience frequent breakdowns – in one instance the minibus just ran out of gas)
or the driver decides that it is no longer profitable to work that route to its end (because there are too few passengers left in the car or too much traffic ahead), so he asks the passengers to get off the bus
Raises the question of what is an acceptable level of variability from the user’s perspective? Even if there is not a lot of variation of itinerary on average, imagine how you would feel if every time you use transit, there was one chance out 14 that the bus or the metro will not go all the way to its final destination…
Uneven distribution of ridership, with two routes carrying half of the station’s total passenger throughput
Very different levels of service, impacting accessibility of destinations along the six routes
Passenger volumes by road segment can be used to target investments or enforcement efforts at critical locations
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8. Vehicle Load Factor Analysis
Background
Methodology
Results
Vehicle Load Factor Analysis
Using this data, we performed three types of analysis to assess the reliability of paratransit services. The two first ones are indicators that are commonly used to assess the quality of service of formal transport services.
We start with headway variability: for each vehicle departing on a route, we calculated the time between that departure and the previous one on the same route. That gives a proxy to estimate how long passengers have to spend waiting at the station, and whether this waiting time is stable throughout the day or varies.
When we aggregated this data for all the 65 routes, it showed that headway tended to be shorter in the morning and in the afternoon (because vehicles fill up quickly during peak hours), and are longer towards the middle of the day (when there is less activity)
But we also observed important differences between routes at the station level as you can see on the graph on the right. This is the profile of headways for the six different routes operating out of the busiest station we surveyed.
Each grey dot is a vehicle departing on a route. On the x axis, it’s the time of the day (from morning on the left to evening on the right). On the y axis it’s the time interval since the previous departure on that route.
You can see that the last route is much less active than the other ones, but overall the interval between vehicles is fairly short, between 10 and 15 minutes on average.
What’s also interesting is to look at the fitted regression lines because it shows that there is complementarity between routes at the station level.
The two first ones are very busy in the morning and gradually slow down throughout the day
Number 3 and 5 have the opposite profile : they are not very active in the morning, but business picks up with the afternoon peak.
That illustrates what I was saying earlier: since there is a fixed number of vehicles working from a station, they have an interest in operating routes a set of routes that have complementary levels of demand throughout the day if they want to minimize idle time and maximize their revenue
No seats available on first part of outbound trip
Passengers have to walk to terminal!
Passengers alight before last stop to avoid congestion close to terminal
Lower load factors on return trip, since vehicles are not allowed to wait at last stop
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9. Rotations and Revenue 5 to 6 daily rotations recorded on average
Background
Methodology
Results
Rotations and Revenue
5 to 6 daily rotations recorded on average
Drivers only drive 3-4 hours every day
Between half and two thirds of work time spent queuing
Average daily revenue is approximately 65 USD
Important disparities in revenue (due to differences in time spent queuing)
Using this data, we performed three types of analysis to assess the reliability of paratransit services. The two first ones are indicators that are commonly used to assess the quality of service of formal transport services.
We start with headway variability: for each vehicle departing on a route, we calculated the time between that departure and the previous one on the same route. That gives a proxy to estimate how long passengers have to spend waiting at the station, and whether this waiting time is stable throughout the day or varies.
When we aggregated this data for all the 65 routes, it showed that headway tended to be shorter in the morning and in the afternoon (because vehicles fill up quickly during peak hours), and are longer towards the middle of the day (when there is less activity)
But we also observed important differences between routes at the station level as you can see on the graph on the right. This is the profile of headways for the six different routes operating out of the busiest station we surveyed.
Each grey dot is a vehicle departing on a route. On the x axis, it’s the time of the day (from morning on the left to evening on the right). On the y axis it’s the time interval since the previous departure on that route.
You can see that the last route is much less active than the other ones, but overall the interval between vehicles is fairly short, between 10 and 15 minutes on average.
What’s also interesting is to look at the fitted regression lines because it shows that there is complementarity between routes at the station level.
The two first ones are very busy in the morning and gradually slow down throughout the day
Number 3 and 5 have the opposite profile : they are not very active in the morning, but business picks up with the afternoon peak.
That illustrates what I was saying earlier: since there is a fixed number of vehicles working from a station, they have an interest in operating routes a set of routes that have complementary levels of demand throughout the day if they want to minimize idle time and maximize their revenue
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10. Discussion A paradoxical situation: Passengers Operators
Background
Methodology
Results
Discussion
A paradoxical situation:
Passengers
Operators
High vehicle occupancy
No pickup along the way
Maximized revenue/km
Large fleet size
No lack of supply
Low turnover
Potential improvements:
Reduce fleet size
Increase number of rotations per vehicle
Reemploy removed drivers and vehicles
Abandon load-and-go system
Pick up more passengers along the way
Covering operating expenses (fuel)
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11. 4/4
Simon Saddier
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