Hebbal once the northern boundary of Bangalore, is now a strategic intersection connecting the north of Bangalore to the centre of the city.
The amount of traffic that the flyover carries everyday is estimated to be 1.4 lakhs a day and is projected to increase to 2 lakhs in a year.
The connectivity that this enables is supreme, carrying traffic from various Tech-Parks from the east and the north, connecting to Yeshwanthpur in the west. Towards the north is Yelahanka and the Bangalore International Airport. Finally, joining south into Bellary Road it takes us to the city centre.
The Hebbal highway, having various access points to the NH 44 highway causes the traffic to come to a halt at the bottleneck, where NH44 extends to a flyover with a reduced number of lanes. With traffic density changing every hour of the day, the factors of the road —access points and the number of lanes, cause a shift in the average vehicular speed away from the actual speed limit of the road. The varying speed of different vehicles on different types of roads causes a non-uniform density of traffic, hence, the non-uniform flow of traffic.
Today’s road rules (speed limits, overtaking, following lanes, etc.) do not adapt to unforeseen circumstances (for example, accidents, phantom intersections, congestion, etc). Speed limits are set, to ensure uniform traffic flow on the roads, to establish a good balance of risk and travel time, and to ensure the safety of all pedestrians, passengers, and drivers. It does happen to be easier to follow them under ideal conditions (such as proper infrastructure, lane behaviour, no accidents). But the bigger problem arises when there is any delay in action by a mass of drivers.
For the safety of the drivers and passengers, the transport department has defined stopping distance, ‘distance in meters need to maintained before the car in front of us while traveling at a certain speed’. It accounts for the time it takes to register than the car in front of us has stopped suddenly (reaction time), break and start again (breaking distance). But we mostly end up tailgating, which is driving close to the car in front of us. The traffic now moves in a slithering formation cased due to the lag in breaking and accelerating while tailgating. This is called a traffic snake. Quick lane changing causes a series of traffic snake causing a phantom intersection– a congested patch of traffic which gives an impression of a cause like, an accident or an actual intersection when there is none.
At Hebbal almost 6 lanes from each side converge into 2 lane bridges. Hence the infrastructural intersections and the phantom intersections together cause the traffic to come to a standstill during prime time when there is a big number of car units per kilometre. Hence, two regulations need to be performed to solve this. 1, Normalise the traffic flow all throughout the nodes leading up to the flyover and 2, maintain uniform density on each node.
Speed Limit
Accident History
Traffic
Distance
Lanes
Traffic signals
Intersections
Types of Vehicles
Road Quality
Contextual Enquiry
Commissioner of Police, Traffic Department, Bengaluru; Officer, Yelahanka Traffic Police Station, Drivers.
Realtime data
Manually recorded real-time traffic data by Google Maps through periodic screenshots all day long on Thursday, 04 October, 2018.
Field Study
Observing driving patterns at Hebbal; Comparing speed limit vs vehicular speed between Cubbon park and Yelahanka; Timing intersection lags.
Dissecting paths at intersections, they are divided into nodes and labelled.
The traffic data here used by google shows the range of traffic distributed between 5 colours representing the weight of traffic which accounts for the traffic density with a node plus the intersection delay calculated by evaluating the realtime data with the historically recorded traffic weight for the particular node at the particular time of the day of the week.
| 8:07 |
| 8:16 |
| 8:25 |
| 8:35 |
| 8:40 |
| 9:00 |
| 9:05 |
| 9:10 |
| 9:15 |
| 9:20 |
| 9:25 |
| 9:30 |
| 9:35 |
| 9:40 |
| MB(Service Road) | H+A (NH4 towards city) | AE(NH44 towards city)2 | AB(service link) | BC(service link) |
| 1 | 3 | 1 | 3 | |
| 2 | 3 | 4 | 3 | 3 |
| 1 | 4 | 4 | 4 | 4 |
| 4 | 4 | 4 | 4 | 4 |
| 4 | 4 | 4 | 4 | 4 |
| 3 | 4 | 4 | 3 | 4 |
| 4 | 4 | 4 | 4 | 3 |
| 4 | 4 | 4 | 4 | 3 |
| 4 | 4 | 4 | 4 | 3 |
| 4 | 4 | 4 | 4 | 3 |
| 4 | 4 | 4 | 4 | 4 |
| 4 | 4 | 4 | 4 | 4 |
| 4 | 4 | 4 | 4 | 3 |
| 3 | 4 | 4 | 4 | 3 |
Yellow (moderate): 65 – 85%
Brown (stand still): 0
The speed recognised by google for each colour on the map is in alignment to the World Traffic Service.
The fitted curve equations for each of these edges can be used to predict the traffic flow at different times of the day. For Example:
Fig 3. (The traffic flow as a function of time for each node is seen graphically, and the fitting curve equations for each were recorded as a polynomial equation of the 10th degree.)
Further we find the equation of the graph of each node. For example, MB (Service Road). This is our variable parameter. We then bring all the constant parameters (as we decided we wont be altering the infrastructure).
As tabulated ahead,
| MB (Service Road) | |
Equation | y = 7E-16×6 – 3E-12×5 + 4E-09×4 – 3E-06×3 + 0.0001×2 + 0.4789x – 126.96 R² = 0.4777 |
| distance (m) | 180 |
| lanes | 2 |
| traffic signals (y-1, n-0) | 0 |
| number of intersections | 3 |
| direction preference (from the city centre) | towards |
| types of vehicles LMV:HMV:Busses | 06:01:03 |
| road quality | 7/10 |
| speed limit : LMV | 50 |
| speed limit : HMV | 40 |
| speed limit : Busses | 65 |
where distance of every node (d);
number of lanes (l);
speed limits for different vehicle categories: 2 wheelers, light and
heavy motor vehicles, buses (s_l_2w, s_l_lmv, s_l_hmv, s_l_buses);
intersections (i);
heights (h);
traffic density (n).
Assuming the infrastructure i.e. distance, lanes, intersections and height as a constraint. We, are left with the traffic density* (n) and the speed limits of LMV, HMV and busses. Thus, by altering the speed limits over nodes would alter the density of traffic over the current and upcoming routes.
With the city of Bengaluru exploring the possibility of elevated corridors to support unmanageable traffic, we propose an alternative, sustainable traffic management model that does not require altering infrastructure and rather uses real-time traffic data to alter the speed limits across various road networks in further yet surrounding areas that affect the congestion at the Hebbal bottleneck. Here, the driver starts to alter his / her driving way in advance rather than at the congested bottleneck.
This dynamic speed limit system can ensure uniform traffic flow and fuel efficiency.
With real time and historical data in our proposed solution, we can foresee the implications of the proposed variable speed limits way in advance, thus aiding the gov and traffic regulators to plan roadblocks and construction plans accordingly.
After all, the ultimate solution to uniform traffic flow is self-driving cars, but we can give us ourselves enough reaction time meanwhile 🙂
This project was accepted and presented at the Citizens for sustainability #ABetterHebbal Challenge 2019 supported by Urban Development Department (UDD, Karnataka)
The traffic data here used by google shows the range of traffic distributed between 5 colours representing the weight of traffic which accounts for the traffic density with a node plus the intersection delay calculated by evaluating the realtime data with the historically recorded traffic weight for the particular node at the particular time of the day of the week.
| 8:07 |
| 8:16 |
| 8:25 |
| 8:35 |
| 8:40 |
| 9:00 |
| 9:05 |
| 9:10 |
| 9:15 |
| 9:20 |
| 9:25 |
| 9:30 |
| 9:35 |
| 9:40 |
Yellow (moderate): 65 – 85%
Brown (stand still): 0
The speed recognised by google for each colour on the map is in alignment to the World Traffic Service.
