Bhargava Rama Chilukuri

Traffic in developing countries has a heterogeneous vehicular mix that uses all the network links and lacks lane discipline. Modeling and controlling such a mixed traffic system are challenging since most of the well-established models were developed for homogeneous traffic. It is hypothesized that segregating the mixed traffic by assigning a unique mode to each link will enhance system capacity. Towards achieving it, this paper proposes optimal mode-route assignment formulations with the objective of minimizing the total system travel time. However, perfect segregation is not always possible since the solution depends on the network topography. A viable solution is to make some links multi-modal. Another formulation is also presented in this paper to address this issue. Both the formulations are demonstrated using sample networks. Linear and nonlinear integer mathematical programming methods are used to explore the qualitative characteristics of optimal mode-route assignment using the single-path routing method. The results indicate that, in the worst case where perfect segregation is not possible, proposed formulation II can identify network with the least number of the multi-modal links. This research will help to develop effective strategies to model, control, and enhance the safety of mixed traffic networks.

Link cost function and link capacity are critical factors in traffic assignment modeling. Popular link cost functions like the Bureau of Public Roads (BPR) function have well-known drawbacks and are not suitable for mixed traffic conditions where a variety of vehicle classes use the road in a non-lane-based movement. Similarly, capacity is generally considered as a constant value. However, in mixed traffic conditions, capacity is not constant, but a function of vehicle class composition. Toward addressing these issues, this paper proposes a link cost function in relation to link travel time and link capacity in relation to vehicular traffic flow for mixed traffic conditions. The functions are developed based on the kinematic wave model, which is popularly used for estimating traffic dynamics on the roads. The developed link cost function and link capacity use field measurable parameters that incorporate mixed traffic features. The functions are validated against empirical data obtained from 12 signal cycles from two different signalized intersections in Chennai, India, representing different scenarios of mixed traffic, and it was found that the results match well with the empirical data.

Restriction of the movement of heavy-trucks in a transportation network is a commonly used strategy to mitigate traffic congestion and pollution issues, especially in congested urban areas. However, the cost and capacity functions used in the literature are inadequate to describe the traffic dynamics in heterogeneous conditions. This paper proposes a systematic framework with new cost and capacity functions, to identify critical links in a network and to determine the minimum cost truck routes in the network by restricting trucks on identified critical links. A hierarchical mathematical programming framework with integer programming formulations are presented. The capacity and cost functions are derived based on a multiclass model and shockwave analysis to represent realistic traffic flow interactions between the trucks and cars. The model results are validated using VISSIM simulation, which shows that the total delay reduced and the network capacity utilization improved with truck restriction.

In the area of Solid Waste Management, transportation of the collected waste is a critical aspect considering the substantial time spent by garbage trucks on public roads. Studies have reported that transporting garbage has challenges related to public exposure and aesthetics. This study presents a generalised bi-objective formulation for the optimal routing of garbage trucks from transfer stations to recycling sites/landfills considering the trade-off between public exposure and aesthetic loss and constraining the operating cost. The formulation uses the novel link capacity function to account for the delay at traffic signals and the mix of trucks and cars on link performance. The proposed formulation is solved using the weighted sum and ε-constraint methods and applied to a realistic case study of the City of Chicago, USA. The Pareto Front obtained for the bi-objective formulation offers diverse trade-off solutions catering to distinct performance metrics. The results highlight the disparity across the solutions; the solution (P0.95 on Pareto Front) for minimum operating cost (or travel time or distance travelled) is very different from the solution (P0.4 on Pareto Front) for aesthetic cost and public exposure. The parametric study indicated that a modest operating budget may suffice for achieving aesthetic benefits, but minimising public exposure requires a higher operating budget. Finally, the proposed framework is adaptable to address various challenges pertaining to waste transportation, thereby serving as a valuable tool for evaluating policies and practices geared towards sustainability objectives.