Maximizing Transport Productivity With HTN’s Novel Driver Comfort Index

Executive Summary​

This White Paper introduces a flow-centric framework for stabilizing mixed traffic environments through HTN’s novel Driver Comfort Index (DCI). DCI is a kinetic-energy–based parameter designed to harmonize the interaction between human-driven and machine-controlled vehicles—two control modes that naturally operate under different traffic flow dynamics.

Mixed traffic instability arises because human drivers intuitively adapt to rising traffic loads through smooth speed moderation, while machine-control algorithms often respond with sharper, non-accommodating decelerations. This mismatch amplifies turbulence in traffic flow.

HTN’s empirical observations and analytical results demonstrate that DCI reduces breakdown probability, increases lane capacity, and decreases acceleration and deceleration noise. These improvements produce measurable environmental and economic benefits, including reduced fuel use, lower emissions, and more efficient roadway throughput. By unifying human and machine driving behaviors under a single proactive flow-management metric, the DCI-based framework establishes a coherent foundation for next-generation Intelligent Traffic Management Systems.

Introduction

Traffic congestion and recurrent flow breakdowns remain persistent obstacles to transportation productivity. As traffic volumes approach saturation, instabilities magnify and propagate backward through the flow, reducing capacity well below the roadway’s physical limit.

To mitigate these instabilities, HTN introduces the Driver Comfort Index (DCI) – a novel parameter that captures the kinetic-energy balance and dynamic spacing behavior inherent to human driving. DCI provides actionable insight into how traffic loads can be accommodated while maintaining flow stability.

HTN’s empirical and analytical studies show that DCI not only increases lane capacity for human drivers but also stabilizes mixed traffic scenarios in which human-driven and machine-controlled vehicles share the roadway. By quantifying comfort-driven flow adaptation, DCI offers a predictive – and ultimately prescriptive basis for traffic optimization.

Related Work

Classical and modern traffic flow theories emphasize the importance of speed variance, reaction delays, and the propagation of pressure waves along vehicle platoons. Models such as Kerner’s two- & three-phase theory [1], and foundational car-following theories, such as the General Motors (GM) model series [2,3], the Intelligent Driver Model (IDM) [4], Gipps’ model [5], and Wiedemann’s simulations [6] establish key relationships between acceleration, spacing, and relative velocity. These publications advanced understanding of these phenomena by characterizing the statistical relationships among speed, headway, and acceleration. However, such approaches remain predominantly reactive: They respond to prevailing conditions rather than shaping them proactively.

Encouraged by Kerner’s findings (2016/17) that classical theory fails to explain emerging traffic flow phenomena [7], HTN’s work departs from the paradigm of statistics-based stochastic traffic flow assessments. Through systemic analysis of real-world driving behavior, HTN finds that human drivers naturally employ a stabilizing pattern of speed moderation as traffic density increases. This emergent behavior exhibits favorable flow properties that can be formalized and orchestrated across both human-driven and machine-controlled vehicles.

By embedding this insight into a unified flow-management framework, DCI transforms intuitive human driving behavior into a proactive, analytically grounded traffic optimization strategy capable of managing increasingly mixed traffic environments.

Problem Definition

Complex traffic phenomena emerge when vehicle density approaches saturation. HTN’s empirical and analytical studies demonstrate that human drivers instinctively reduce their speed as inter-vehicle spacing decreases, enabling the flow to absorb additional traffic loads – a process we define as load-accommodating speed reduction. This stabilizing mechanism persists until spacing is compressed to the limits of human comfort and physical feasibility. Beyond this point, further load accumulation triggers inevitable traffic breakdown.

However, full breakdown is often preceded by two forms of premature instability that prevent traffic from reaching its maximum possible lane capacity:

(1) Lateral Disruption From Heterogeneous Spacing Preferences

Drivers comfortable with short headways tend to enter the larger gaps maintained by drivers who prefer longer spacing. These lane changes create local turbulence that spreads laterally across multiple lanes, undermining flow stability well before saturation is reached.

(2) Incompatibility Between Human and Machine Driving Dynamics

In today’s mixed traffic, human drivers and machine-control systems employ fundamentally different flow strategies. Humans tend to absorb increasing traffic loads through gentle, anticipatory speed adjustments, whereas machine-controlled vehicles often apply sharper, non-accommodative deceleration routines.
These machine-induced disturbances introduce premature instabilities that can trigger immediate breakdowns, even when sufficient capacity remains available.

As machine-controlled vehicles become more prevalent, their disproportionate influence on flow instability – known as mega-instability – is poised to become a dominant challenge in modern traffic management.

Theoretical Framework

HTN’s empirical and analytical studies show that human drivers’ intuitive load-accommodating behavior closely mirrors the classical kinetic-energy relationship, in which a moving body’s momentum and required braking force scale with the square of its velocity. Because the distance required to stop a vehicle increases quadratically with speed – a relationship widely known as the Quadratic Function of Velocity (QFV). Because of this natural non-linear relationship between following distance and velocity, lane capacity adjusts in a nonlinear relationship with speed as well.

Although this may appear to reduce flow efficiency, the opposite is true: QFV-based intuitively adjusted spacings allow traffic to accommodate higher vehicle counts – albeit at lower speeds – thereby preserving flow stability under near and fully saturated traffic conditions.

4.1 Preferred Inter-Vehicle Spacing (PIS)

To formalize this behavior, HTN introduces the Driver Comfort Index (DCI), that captures the human driver’s internalized sense of safety, braking capability, perceived risk, and environmental conditions.[8] Within this framework, a driver’s Preferred Inter-Vehicle Spacing (PIS) under saturated flow conditions can be modeled as:

PIS      =          1 / DCI   ×   Velocity²

This expression mirrors the empirically observed QFV relationship – that spacing grows quadratically with speed but is moderated by the DCI value, which represents a driver’s “tolerance envelope” for managing kinetic energy at different velocities.

4.2 Center-to-Center Distance (CCD)

For system-level traffic flow evaluation, the fundamental spacing unit is the Center-to-Center Distance (CCD) between vehicles. CCD incorporates the physical vehicle length, expressed as the Average Car Length (ACL):

CCD   =          1 / DCI   ×   Velocity²   +   ACL

4.3 Interpretation of DCI in Relation to Classical Mechanics

Newtonian kinetic energy includes the mass of the moving object, whereas HTN’s formulation replaces mass with DCI, which captures the driver’s subjective risk perception and anticipated braking performance under varying conditions. DCI therefore functions as a behavioral analog to vehicle mass.

The DCI parameter’s multidimensional characteristic is reflected in its mathematical dimension – mph²/ft – which is a complex combination of velocity (mph), acceleration & deceleration (ft/sec²), traffic density (vehicles per mile, vpm), and lane capacity (vehicles per hour. vph).

Reframing of the classical kinetic energy law enables a human-centered, flow-centric interpretation of classical mechanics within real traffic environments.

4.4 Capacity Under DCI-Governed Saturated Flow

Using Greenshields’ classical linear speed–flow relationship (1935) as a foundation, HTN adapts the formulation to produce a non-linear lane capacity expression consistent with DCI-governed saturated flow dynamics.

The resulting model yields:

                                                5280   ×    DCI    ×   Velocity

Lane Capacity _(DCI)   =          __________________________

                                                 Velocity²    +   DCI    ×    ACL

This equation describes the practical lane capacity when vehicle control is dominated by the DCI-adjusted kinetic-energy-based spacing mechanism.

In comparison, machine control typically uses time headway (THW) – an interval-based control mechanism – that results in constant lane capacity across the entire traffic flow velocity spectrum.

            Lane Capacity _(THW) =          3600   /   Interval

Both lane-capacity profiles are shown in the adjacent chart. The gap between high-DCI and low-DCI drivers demonstrates that variations in preferred following distance can exceed 300%, underscoring the strong incentive for disruptive lane-changing behavior of drivers with higher DCI values.