Evaluation of Highway Capacity for National Road Network in Sri Lanka

The ability to accurately estimate lane capacity and Level of Service (LOS) is a key element in highway planning and management, which is essential to develop an efficient road network. The current methodologies to estimate capacity and level of service do not satisfactorily incorporate the traffic and roadway characteristics in Sri Lanka.  

The estimated capacity for a two lane roads in Sri Lanka is around 2450 pcu/hr [1], the free flow speeds of the two lanes that were used to develop the traffic flow model was around 40 km/h  indicating the distinct roadway design and land use characteristics. The capacity values of four-lane highway sections derived using an empirical method ranged from 2399 pcu/h/lane to 1346 pcu/h/lane [2] indicating siginificant impact from traffic flow and roadway conditions on its capacity.  The flow-speed models developed based on 5-minute and 15-minute interval traffic flow data resulted in at capacity value of ~2500 PCU/h/lane (q = u {ln (60)-ln (u)} /0.008) and ~2300 PCU/hr./lane (q = u {ln (56.5)-ln (u)}/0.009), respectively. The capacity flows were derived at a speed of around 20-25 km/h. The reason for the reduction being the inability of the traffic stream to sustain capacity flow for longer periods of time especially at trivial speeds. The models also revealed the speed-density follows Greenwood's non-linear model and the free flow speeds in urban multi-lane roads are around 55 km/h [3]. The impact of roadway factors on urban road capacity are primarily due effective lane width (considering parking, roadside activities etc.), access point density, median type, and built environment condition. For example, capacity (pcu/h/lane) of a four lane road can be defined as,  C = 1467 + 190 C_L + 118 C_M − 39 C_A − 206 C_B, where, ,C_L is the effective lane width (m), C_M is the median Type (0,1), C_A is the access point density (per 400m section), and C_B is the built environment type (0, 0.7, 1) [4].  

Most of capacity estimation methods based on deriving the maximum flow or throughput using a traffic speed-flow mode does not give a realistic capacity estimate as they cannot be sustained for long, under normal flow conditions. The breakdown probability approach results in a capacity reduction of around 10% in limited access multi-lane road sections and up to 30% in sections with no significant access management measures. Moreover, the speed at capacity conditions are around 30 km/h compared to 20 km/h derived from the empirical traffic flow models [5]. In addition to roadway characteristics, traffic composition, road capacities are affected by road side parking movement rates due to the delays on through traffic [6] and the road surface conditions due its impact on vehicle speeds [7]. 

Traffic flow (q) is a function of the vehicle headway (h), q = 1/ h. Headway is not only important to identify the flow characteristics, its an important indicator for evaluation of traffic safety. Headway distribution pattern vary as flow change from free flow conditions to capacity flow conditions as well as depending on the vehicle combinations [3,8].  

Low Volume Road Network Asset Management

Low-volume rural roads are the lifeblood of developing economies, providing essential connectivity that links isolated communities to markets, healthcare, and education. Despite their massive socioeconomic footprint, these roads often suffer from rapid deterioration due to ad hoc decision-making, chronic underfunding, and a lack of technical resources [1]. Traditional pavement management systems require extensive, costly data collection that frequently overwhelms local agencies. To bridge this gap, our research focuses on developing customized, cost-effective, and data-driven asset management frameworks tailored specifically for rural road networks [2, 3]. By analyzing the entire asset management lifecycle, these frameworks provide practical tools that local authorities can use to optimize their limited resources.

1. Road Design and Pavement Selection

A major cause of insufficient maintenance funding is the poor selection of pavement types during initial road construction, which often leads to rapid deterioration [1]. To address this, a comprehensive framework for selecting pavement types has been introduced that moves beyond traditional, traffic-volume-only criteria [4]. This framework incorporates technical constraints alongside vital socio-environmental factors, including adjacent land use, terrain, connectivity, and the expectations of local communities [1].

2. Road Condition Monitoring

Traditional, data-intensive pavement surveys are too expensive and complex for local provincial agencies. To create a sustainable alternative, recent studies have validated the applicability of frontier technologies—specifically, smartphone-based roughness data collection [5]. Comparisons between smartphone data and conventional roughness measurement equipment (such as the Bump Integrator) demonstrate that smartphone sensors offer a highly accurate, cost-effective method for network-level pavement condition evaluation [5, 6].

3. Performance Evaluation

To accurately forecast future road conditions, pavement roughness prediction models have been tailored specifically for local highways [7]. Because conventional level-of-service metrics often focus strictly on traffic operations, a simplified Highway Performance Index has also been formulated [8]. This index evaluates functional performance based on easily measurable roadway characteristics, such as carriageway width and the condition of highway structures, allowing provincial agencies to effectively assess the upgrading needs of their networks [8].

4. Prioritization and Optimization of Maintenance

In rural networks, allocating maintenance funds based solely on pavement condition ignores the road's broader value to the community. A Multi-Objective Optimization (MOO) approach using Genetic Algorithms has been proposed to balance budget constraints with network performance [9, 10]. Crucially, the introduction of a Socio-Economic Priority Index allows for ranking roads based on network connectivity, importance to the community, land-use patterns, and traffic volume [9, 10].

5. Incorporating Safety

Road safety in rural areas is frequently neglected in maintenance planning due to a lack of reliable crash data [11, 12]. To overcome this, methodologies have been developed to quantify safety risks using a Cumulative Safety Index (CSI) derived from multidisciplinary road safety audits [11]. By integrating the CSI into a linear programming and MOO framework alongside pavement roughness (IRI), agencies can now logically select optimal safety treatments within a strict annual budget [12, 13].

6. Climate Impacts and Resilience

With the increasing intensity of climate events, rural roads are highly vulnerable to weather-induced damage. Current asset management research incorporates climate resilience by establishing a Climate Risk Index (CRI) that evaluates the probability of climate events, topographic vulnerabilities, and existing pavement severity [14]. Furthermore, a network robustness framework for post-flood recovery has been designed to identify the most cost-effective repair strategies for flood-damaged road links [15, 16].

7. Fund Allocation Strategies

Strategic fund allocation is the cornerstone of effective asset management. The frameworks developed across these studies utilize advanced mathematical modeling—ranging from Integer Programming to Heuristic Genetic Algorithms—to maximize limited financial resources [2, 17]. These models allow decision-makers to optimally divide funds between immediate corrective maintenance and long-term preventive maintenance [2].

8. Prioritization Methods for Road Upgrading

A significant risk in rural road management is "overcapitalization"—where the cost of upgrading a road exceeds its economic and social return [17]. To prevent the wasteful deployment of public funds, a data-driven screening methodology using Principal Component Analysis (PCA) and K-means clustering has been established [17]. This categorizes projects into distinct clusters, easily identifying roads with strong economic returns versus low-demand roads. An early-stage predictive model also flags projects at risk of falling below a 6% economic return threshold, ensuring strategic, high-impact investments [17].

References

Highway Work Zone Analysis

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