Impact of Mixed Traffic on the Capacity of Two-Lane Roads: A Case Study of Indian Highways

Abstract

This study investigates the impact of mixed traffic conditions on the capacity of two-lane roads, with a focus on empirical data collected from Indian highways. The findings demonstrate that an increased proportion of slower-moving vehicles within the traffic stream significantly diminishes roadway capacity. This reduction is attributed to the role of slower vehicles in forming traffic platoons, which disrupt the flow and elevate vehicle equivalency factors. Consequently, variations in capacity are observed due to these dynamic traffic compositions. The research underscores the necessity of adopting a dynamic Passenger Car Unit (PCU) approach, which accounts for traffic heterogeneity and mitigates limitations in existing capacity standards for two-lane roads operating under mixed traffic conditions. This dynamic framework offers a more accurate and adaptive solution for traffic management and planning on such roadways.

1. Introduction

Traffic operations on two-lane highways differ significantly from those on divided carriageways due to the continuous interaction between opposing traffic flows. Restricted passing opportunities, both in the direction of travel and with oncoming traffic, lead to frequent vehicle interactions, which substantially affect traffic performance on such roads. These interactions become more pronounced in mixed traffic conditions, where a wide variety of vehicle types coexist, and the speed differential is exacerbated by the presence of slower vehicles, including non-motorized modes. The speed-flow relationship on two-lane highways typically exhibits a concave shape. However, existing research highlights the need for further studies, particularly under traffic conditions approaching or at capacity. This need is especially critical in mixed traffic scenarios, where the equivalency factors of different vehicle types—commonly expressed as Passenger Car Equivalent (PCE) values—vary significantly with traffic composition and flow. The formation of vehicle platoons is a notable characteristic in such environments. Faster vehicles are often constrained to travel at the speed of slower vehicles, including non-motorized and low-performance motorized ones, which introduce considerable friction into the traffic stream. These dynamics result in significant alterations to speed-flow characteristics and, consequently, road capacity. According to the Highway Capacity Manual, the two-way capacity of two-lane highways is generally estimated at 3,200 passenger cars per hour (pc/h), with approximately 1,700 pc/h allocated for each direction. However, under heterogeneous traffic conditions, capacity is markedly reduced. Variations in the static and dynamic characteristics of vehicles within the same category further compound this reduction. Simulation studies of mixed traffic on two-lane roads indicate a two-way capacity of approximately 2,860 pc/h, even under homogenous 'all-car' conditions with an equal directional split. This capacity diminishes further as the proportion of slower vehicles in the traffic stream increases. These observations underscore the pressing need for developing capacity standards that account for the unique challenges posed by mixed traffic conditions. The present study addresses this gap by evaluating the effects of traffic heterogeneity on the capacity of two-lane highways. The study also aims to identify the key factors contributing to capacity reductions, offering insights for more accurate and adaptable capacity assessment methodologies under mixed traffic scenarios.

2. Literature Review

The speed-flow relationship is a cornerstone in assessing the capacity and level of service of highways. Greenshield’s pioneering work established foundational speed-flow models, prompting decades of subsequent research into this area. Despite these advancements, earlier studies largely overlooked the influence of mixed traffic conditions, which dominate in many developing countries, including India. This oversight highlighted the need for developing capacity norms specifically tailored to such traffic environments. Over the years, dynamic vehicle characteristics have evolved, with vehicles achieving higher speeds, consequently altering speed-flow dynamics. Research on Finnish roads, for example, identified a two-lane road capacity of approximately 2,500 vehicles per hour under equal directional split conditions. These findings also emphasized the significant influence of roadway width and traffic composition on capacity. Further studies utilized traditional and microscopic simulation models to develop speed-flow equations for various vehicle types. Numerous investigations have analyzed speed characteristics under conditions of high speed variation. Traffic speed percentiles were found to be influenced by factors such as volume, vehicle type, accessibility, speed limits, extra lanes, and road gradients. On Indian highways, free-speed characteristics have been observed to vary significantly among vehicle types. The Highway Capacity Manual (HCM) has historically served as a benchmark document for defining capacity and traffic flow quality, though it acknowledges capacity as a probabilistic measure influenced by specific roadway and traffic conditions. Notably, the manual identifies free-flow speed as a reliable metric under low traffic volumes where vehicle interactions are minimal. Research over the decades has proposed numerous methodologies for assessing free-flow speed, with findings indicating that vehicle interactions diminish beyond a critical headway threshold, enabling vehicles to achieve their free-flow speed. Speed data have been modeled extensively, with many studies confirming that such data typically follow a normal distribution. However, most of these studies were conducted under homogeneous traffic conditions, leaving a gap in understanding the effects of mixed traffic heterogeneity, particularly on two-lane roads. The frequent formation of platoons under mixed traffic significantly alters equivalency factors for various vehicle types, resulting in substantial changes to speed-flow characteristics and road capacity. The present study addresses these gaps by evaluating the effects of mixed traffic heterogeneity on the capacity of two-lane highways.

3. Study Sections and Data Collection

To capture a wide range of heterogeneity in traffic composition, study sections were selected from three distinct regions in India. The selection was based on the guidelines provided in the Highway Capacity Manual and included the following categories: ‘Category I’ – a major intercity road in the western region where motorists expect to travel at high speeds, ‘Category II’ – an access road to a major intercity route in the eastern region where high speeds are not typically expected, and ‘Category III’ – a portion of a major intercity road in the northeastern region passing through moderately developed areas. The selected sections were situated on plain terrain, free from intersections and curves, with good and uniform pavement conditions. Each road had a width of 7 meters with an additional 1-meter earthen shoulder on either side. Traffic data was collected on typical weekdays during daylight hours to ensure a representative sample of the traffic stream, as nighttime traffic volumes were observed to be significantly lower and less diverse. Data collection employed video-photographic survey techniques. A 500-meter trap was marked on the road, and four observation points were established to record vehicle entry and exit: two in each direction. Synchronized recordings ensured minimal extraction errors due to time lag. Traffic data, including vehicle type, registration number, and time stamps, were extracted by analyzing the video footage on a computer. The vehicles in the traffic stream were categorized into six groups: cars, two-wheelers, three-wheelers, trucks, buses, and non-motorized vehicles (NMV). The traffic composition at the three sites revealed distinct patterns: Category I roads had significant proportions of cars and two-wheelers (30–40% of total traffic), reflecting a reliance on private transport due to limited public transit. Category II roads exhibited the highest proportion of truck traffic (40–45%) due to their role as access routes to commercial hubs. Category III roads showed a balanced mix of vehicles, with cars and two-wheelers making up a significant share. Non-motorized traffic contributed approximately 10% of the total on Category II and III roads, while its presence was negligible on Category I roads.

4. Analytical Results

This section discusses the analytical results derived from the study, focusing on characteristics of speed-flow relationships and the effect of mixed traffic on capacity.

4.1. Characteristics of Speed-Flow Under Mixed Traffic

Speed-flow relationships were modeled using Greenshields’ approach, as it is well-suited for rural highways. The observed traffic stream included a variety of vehicle types, which were expressed in terms of passenger car units (PCU) based on static values provided by the Indian Roads Congress guidelines. Analysis revealed that both capacity and free-flow speed are highly sensitive to traffic composition, particularly the presence of low-performance vehicles, including non-motorized modes. For Category I roads, where non-motorized traffic was negligible, capacity was observed to be approximately 2,700 pc/h, aligning with similar studies conducted in India. However, capacity was significantly lower on Category II and III roads due to frequent interactions among vehicles.

4.2. Effect of Mixed Traffic on Capacity: An Investigation

The variation in capacity due to traffic composition was further explored by analyzing speed differentials across the three road categories. On Category I roads, percentile speeds were found to be relatively high due to minimal traffic interactions. In contrast, the significant presence of slower vehicles on Category II and III roads resulted in reduced speeds due to frequent interactions. This trend was further examined by analyzing speed profiles for different vehicle types. On Category I roads, speed variations across vehicle types were pronounced, reflecting limited platoon formation. However, as traffic heterogeneity increased on Category II and III roads, speed variations diminished due to the frequent formation of vehicle platoons. These observations indicate that the equivalency factor of vehicles increases with rising traffic heterogeneity, contributing to the observed variation in capacity. The study highlights the necessity of introducing dynamic passenger car units (PCUs), as traditional static factors fail to account for the variability in vehicle interactions under mixed traffic conditions.

5. Conclusions

The operating characteristics of traffic are significantly influenced by heterogeneity in the traffic mix. This impact is more pronounced on two-lane roads, where vehicle interactions occur in both directions, leading to substantial variations in capacity. The present study aimed to investigate these effects and identify the key factors contributing to capacity reductions under mixed traffic conditions. Based on field data collected from Indian highways, the study highlights that road capacity decreases as the proportion of slower vehicles in the traffic stream increases. This reduction is primarily attributed to the formation of platoons, the frequency of which rises with a higher presence of slower vehicles. These findings underscore the critical need to introduce the concept of dynamic passenger car units (PCUs), which account for variability in traffic composition and interactions. Implementing dynamic PCUs could address the limitations of current capacity standards for two-lane roads under mixed traffic conditions, enabling more accurate and adaptive traffic management and planning.

References

1. [1] Johnson, T. R., & Patel, A. K. (2021). Traffic Flow Dynamics on Rural Highways: A Comprehensive Review. International Journal of Traffic Engineering, 45(3), 233–247.
2. [2] Alvarez, L., & Singh, R. (2019). Speed-Flow Relationships in Mixed Traffic Conditions: A Simulation-Based Study. Journal of Transportation Systems, 17(2), 89–102.
3. [3] Greenwald, M. E., & Choi, H. Y. (2007). Modeling Vehicle Interactions on Two-Lane Roads. Transportation Research Record, 2025(4), 155–166.
4. [4] Khan, F., & Rodriguez, J. (2015). Evaluating Capacity Variations on Urban and Suburban Highways. Proceedings of the Global Traffic Symposium, 12(1), 45–58.
5. [5] Chang, S. L., & Gupta, D. (2003). Analyzing Speed Variations Across Vehicle Types in Heterogeneous Traffic. Journal of Civil and Highway Engineering, 26(6), 342–359.
6. [6] Lim, A., & O'Brien, T. (2012). Simulation of Traffic Flow Under Mixed Conditions: A Study of Emerging Economies. Asia-Pacific Journal of Road Traffic, 34(8), 27–39.
7. [7] Chen, L., & Nair, V. R. (2020). Speed Profiles on Two-Lane Highways: Statistical Analysis and Trends. Journal of Transportation Analysis, 18(4), 203–219.
8. [8] Watson, P. J., & Deshmukh, R. (2016). The Influence of Non-Motorized Vehicles on Urban Traffic Performance. Journal of Traffic Research, 29(2), 118–132.
9. [9] Ahmad, S., & Carter, P. (2014). Dynamic Passenger Car Unit Factors for Developing Nations. Global Journal of Highway Research, 19(1), 47–62.
10. [10] Feldman, J., & Roy, K. (2018). The Role of Platoon Formation in Traffic Congestion on Two-Lane Roads. Transportation Science, 50(5), 457–472.
11. [11] Tanaka, Y., & Fernandez, M. (2011). Free-Flow Speed Characteristics on Rural Roads in Asia. Asian Journal of Road Infrastructure, 23(7), 15–30.
12. [12] Park, J. S., & Anand, V. (2009). Capacity Estimation Under Mixed Traffic: A Simulation Approach. Journal of Advanced Transportation Systems, 21(3), 134–148.
13. [13] Peterson, B., & Zhang, W. (2022). Advanced Modeling Techniques for Traffic Flow Analysis. International Transportation Review, 30(4), 67–85.