(Shrestha, 2018) have developed concepts of criticality and redundancy to explain the impact of seasonal disruptions on road network performance within a network analysis. Because these network-scale analyses approach seasonal impacts in terms of binary open-closed changes in roads’ status they obscure quantitative changes in network and road-scale performance from reduced speeds over damaged but still-functioning roads. Many are also data intensive and difficult to replicate in rural, developing world contexts. Where seasonal access is considered by social sciences researchers, they tend to rely on survey data, as in the model of seasonal market access in Ethiopia by Hirvonen et al. (2017). In Nepal, Huber (2015) adjusts speeds for major roads and airports for the monsoon based on expert guidance.

Walking travel must be modeled in rural areas of the developing world where lack of roads or simple poverty compel many to travel long distances on foot to reach services or transport networks. They can therefore represent the entire travel time to a service or be integrated with road transport for a multi-modal accessibility model. Representations of walking speeds vary in sophistication: while the hiking functions of Tobler (1993) and Van Wagtendonk and Benedict (1980) provide foundations for modeling walking speeds, they translate better to cost-time modeling, which treat walking cells like any other, than network analysis, which ignores off-network travel. In practice planners and researchers tend to ignore or simplify walking times in their analysis of rural roads given roads’ primacy in most economic and transport systems. In Nepal, Devkota et al. (2012) modeled walking as a primary transport modality when recommending rural trail bridge investments. To the authors’ knowledge however, the model we adapt here (from Banick and Kawasoe (2019)) remains the only published, scalable model of travel times which incorporates monsoon impacts and walking speeds in a rugged and sometimes high-altitude context.

# 2.3. Traditional rural transport valuation

Since available budgets for rural road building rarely match the observed needs, transportation planners and policy makers must choose the most productive subset of roads from a collection of potential investments. Valuation and prioritization exercises for rural roads investments traditionally define such productivity according to economic cost-benefit analysis (CBA). Van der Tak and Anandarup (1971) framed the classic method of evaluating rural roads construction benefits in terms of welfare gains within a CBA, as measured by society’s net savings from reductions in the cost of supplying goods, increased employment, and increases in commercial traffic linked to new roads. Improvements in travel times are principally used to measure changes in vehicle operating costs and opportunity costs of lost labor time. Lebo and Schelling (2001) updated this method to better emphasize year-round access to rural areas by incorporating seasonal access cost variations, the monetary value of improved access to services, and improved valuation of time savings into traditional cost-benefit analysis. As noted by Belli et al. (2001), the large budgets and timeframes required for these data-intensive methods make them difficult to scale to large sets of roads or road alternatives. Accordingly, Lebo and Schelling argued for analyzing roads’ cost-effectiveness – relative to stated development objectives, instead of economic outcomes – when evaluating lightly trafficked but locally critical rural transport infrastructure where comprehensive CBA may be prohibitively costly.

Accessibility indicators based on travel times may complement or outperform CBAs in rural areas as they present easily parsable decision criteria to lightly resourced rural governments, scale easily across large sets of roads, and readily translate to monetary opportunity costs of travel. Additionally, the travel time indicators generated by cost-time models have demonstrated correlations with changes in fields as varied as conservation (Nelson and Chomitz, 2011), poverty reduction (Porter, 2007; Ahlstr ̈om et al., 2011), trade (Schmitz et al., 2012), and disease prevention (Gilbert et al., 2014). For these reasons and the aforementioned difficulty of appropriately resourcing CBAs for many roads over large spatial scales, we evaluate roads according to their cost-effectiveness versus accessibility indicators in this paper.

# 2.4. Optimization approaches to (rural) transport planning

Previous sections detailed examples of criteria considered when planning roads development, and the manners in which the benefits of proposed projects may be quantified – specifically in a rural developingworld context, with a focus on accessibility. It is only natural that more than one potential approach (e.g. candidate development plan) for the upgrading of a roads network exist (the work of Scaparra and Church (2005) provides excellent examples of this), from which it is necessary to identify at least one which provides superior trade-offs (in cost, accessibility improvement, etc.) compared to the others. This section discusses 6