A Coordinated Routing Approach for Enhancing Bus Timeliness and Travel Efficiency in Mixed-Traffic Environment

This paper proposes and validates a real-time coordinated routing strategy using connected and automated vehicles (CAVs) in mixed-traffic environments to dynamically reroute them away from dedicated bus lanes, thereby simultaneously improving bus schedule adherence and CAV travel efficiency.

The Gist

Imagine a busy city street as a giant, living river. Usually, this river is chaotic: fast cars, slow trucks, and buses all jostling for space, causing traffic jams that make everyone late.

Now, imagine we have two special groups of vehicles:

1. The Buses: They are like the scheduled ferries. They have a strict timetable and must arrive at specific docks (bus stops) on time to keep the city moving.
2. The CAVs (Connected and Automated Vehicles): These are smart, self-driving cars. They can talk to each other and the traffic lights, making them incredibly efficient if they can drive together in a tight, smooth formation.

The Problem: The "Shared Lane" Dilemma

Cities want to build special lanes just for these smart cars (CAVs) to let them zoom ahead. But building new lanes is expensive and hard. So, cities are thinking: "What if we let the smart cars share the existing Bus Lanes?"

Here's the catch: If too many smart cars crowd the bus lane, they might block the buses. The buses get stuck, miss their schedules, and the whole public transit system slows down. But if we ban smart cars from the bus lane, we lose the efficiency benefits of letting them drive together.

It's like a dance floor: If the dancers (CAVs) get too rowdy, they knock over the lead singer (the Bus). If the singer can't move, the show stops.

The Solution: The "Smart Conductor"

This paper proposes a Coordinated Routing Approach. Think of this system as a super-smart traffic conductor standing on a tower, watching the whole city in real-time.

Here is how it works, step-by-step:

1. The Crystal Ball (Prediction): The conductor doesn't just look at traffic right now; they look a few minutes ahead. They know exactly where the buses are going and when they will arrive at the next intersection.
2. The "Tension" Detector: The system constantly checks the bus lane. It asks: "If 10 smart cars enter this lane in the next 30 seconds, will they create a traffic jam that makes the bus late?"
3. The Gentle Nudge (Rerouting): If the system sees a potential jam forming, it doesn't stop the cars. Instead, it sends a gentle message to a few specific smart cars: "Hey, the bus is coming up fast. Instead of taking the fast bus lane, please take the slightly longer regular road for a moment to let the bus pass."
4. The Win-Win:
   • The Bus gets a clear path and arrives exactly on time.
   • The Smart Cars avoid getting stuck in a jam behind the bus. Even though they took a slightly different route, they actually arrive faster because they didn't get stuck in a traffic snarl.
   • The Regular Cars (Human-driven) benefit because the overall traffic flow is smoother.

The Analogy: The River and the Raft

Imagine the bus lane is a narrow, fast-moving river where a large raft (the bus) must pass through.

• Old Way: If you let hundreds of small kayaks (CAVs) into the river, they might block the raft, causing a logjam.
• New Way: The "Smart Conductor" sees the raft coming. It tells a few kayaks to paddle into a side stream (the regular road) just for a minute. The raft glides through the main river without hitting anything. Once the raft passes, the kayaks can rejoin the main flow. Everyone gets to their destination faster.

What the Computer Simulation Showed

The researchers tested this idea using a digital city (a video game-like simulation called SUMO) based on real streets in San Francisco.

• Without the system: Buses were often late, and cars got stuck in traffic.
• With the system:
  • Bus Punctuality: Jumped from about 23% on-time to 90% on-time. The buses became incredibly reliable.
  • Car Efficiency: The smart cars actually traveled faster overall because they avoided the chaos of getting stuck behind buses.
  • The "Sweet Spot": The system works best when there are enough smart cars to make a difference, but not so many that they overwhelm the system.

The Bottom Line

This paper suggests that we don't have to choose between "fast self-driving cars" and "reliable buses." By using real-time data to act like a smart traffic conductor, we can let them share the road. The result is a city where the bus always arrives on time, and the self-driving cars zoom through without getting stuck in the very jams they were supposed to avoid. It's a win for everyone.

Technical Summary

Here is a detailed technical summary of the paper "A Coordinated Routing Approach for Enhancing Bus Timeliness and Travel Efficiency in Mixed-Traffic Environment."

1. Problem Statement

The paper addresses the challenge of managing mixed-traffic environments during the transition to fully autonomous fleets. Specifically, it focuses on the trade-off between:

• Bus Schedule Adherence: Ensuring buses arrive on time to maintain public transit efficiency.
• CAV Travel Efficiency: Maximizing the speed and capacity benefits of Connected and Automated Vehicles (CAVs).

Context:

• Dedicated Lanes (DLs): Traditionally, DLs are reserved for buses to ensure reliability. However, constructing new lanes is costly. A practical alternative is converting existing General-Purpose Lanes (GPLs) into Joint DLs shared by buses and CAVs.
• The Conflict: While Joint DLs increase capacity, uncoordinated CAV usage can cause congestion, leading to bus delays. Conversely, restricting CAVs too strictly reduces their efficiency.
• The Gap: Existing research often focuses on lane-level coordination or static scheduling. There is a lack of dynamic, network-level routing strategies that balance real-time congestion mitigation with reliable bus operations under time-varying demand.

2. Methodology

The authors propose a Coordinated Dynamic Routing Approach that utilizes real-time traffic data to dynamically reroute CAVs.

A. System Modeling

• Network Representation: The urban road network is modeled as a directed graph $G=(V, E)$, where nodes include intersections ($I$) and bus stations ($S$).
• Vehicle Types:
  • Buses: Restricted to Joint DLs, following fixed routes and timetables.
  • Human-Driven Vehicles (HVs): Restricted to GPLs.
  • CAVs: Flexible; can switch between Joint DLs and GPLs to optimize travel time while minimizing interference with buses.
• Traffic Flow Estimation:
  • The system uses the Bureau of Public Roads (BPR) function to estimate travel times based on real-time flow ($f$) and capacity ($c$).
  • Monitoring Windows: The system defines time windows ($\Delta T$ for DLs, $\Delta \tilde{T}$ for GPLs) to monitor cumulative traffic flow. It predicts the number of CAVs entering specific edges within these windows to anticipate congestion.

B. The Coordinated Routing Algorithm

The approach operates in two main phases:

1. Identification of CAVs for Rerouting:

   • The system continuously monitors the estimated arrival time of buses at upcoming intersections.
   • If the anticipated travel time on a Joint DL edge exceeds a threshold (Free-flow time $\times (1 + \lambda)$), indicating potential congestion that would delay the bus, the system triggers rerouting.
   • It identifies the specific set of CAVs currently approaching or on that edge that need to be diverted.

2. Route Optimization:

   • For the identified CAVs, the system re-optimizes their routes from their current intersection to their destination.
   • Objective: Minimize the total anticipated travel time for the rerouted CAVs.
   • Constraints: The new routes must avoid the congested Joint DL edge to protect the bus, while utilizing available GPLs or other non-congested DL segments.
   • Solver: A prediction-aware Dijkstra algorithm is used, where edge weights are dynamic (based on real-time estimated travel times) rather than static free-flow times.

3. Key Contributions

1. Joint DL Modeling: The paper models a mixed-traffic scenario where buses and CAVs share dedicated lanes, explicitly addressing the conflict between maximizing CAV flow and maintaining bus priority.
2. Dynamic Rerouting Framework: Unlike static scheduling, the proposed method uses a real-time, event-triggered mechanism. It only reroutes the minimal necessary set of CAVs when potential congestion is detected, balancing system efficiency with transit reliability.
3. Predictive Traffic Estimation: The integration of a monitoring window and flow accumulation logic allows the system to anticipate congestion before it occurs, enabling proactive rather than reactive routing.

4. Simulation Results

The approach was validated using SUMO (Simulation of Urban MObility) with a realistic network topology based on Van Ness Avenue in San Francisco.

• Experimental Setup:

  • 10 buses with a fixed timetable.
  • Mixed traffic with varying CAV penetration rates (10%, 30%, 50%).
  • Comparison against two baselines: Static Route Planning (SRP) (no rerouting) and Dynamic Route Planning (DRP) (reactive rerouting based on current conditions).

• Key Findings:

  • Bus On-Time Performance:
    • SRP: ~23% on-time rate.
    • DRP: ~57% on-time rate.
    • Proposed Method: 90% on-time rate across all stations.
    • Result: The proposed method nearly eliminated bus delays compared to baselines.
  • Travel Efficiency (CAVs & HVs):
    • The proposed method achieved the lowest total travel time for both CAVs and HVs.
    • Interestingly, the reactive DRP approach resulted in longer travel times than SRP for CAVs due to "secondary congestion" caused by simultaneous rerouting. The proposed method's predictive nature avoided this.
  • Penetration Rates: Benefits saturated between 30% and 50% CAV penetration, suggesting the strategy is robust across different adoption levels.
  • Joint DL vs. Exclusive DL: Allowing CAVs to share the DL (with coordination) reduced travel times for CAVs and HVs significantly compared to an exclusive bus lane, without compromising bus travel times.

5. Significance

• Transit Reliability: The study demonstrates that it is possible to share dedicated infrastructure between public transit and automated vehicles without sacrificing the punctuality of buses, a critical factor for public transit adoption.
• Infrastructure Efficiency: It provides a practical pathway to utilize existing lanes for CAVs without the prohibitive costs of building new infrastructure, effectively increasing network capacity.
• System-Wide Optimization: By proactively managing CAV flows, the approach prevents the "tragedy of the commons" where individual vehicle optimization leads to collective gridlock, thereby improving overall urban mobility.

In conclusion, the paper presents a viable, data-driven strategy for the transitional era of autonomous driving, proving that coordinated routing can harmonize the needs of public transit and automated mobility.