Multi-Factor Dynamic Pricing for Indian Logistics: Festival Calendar, Vehicle Profiles, and Route Difficulty in Real-Time Freight Rate Optimization
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2026
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| _version_ | 1866901539402022912 |
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| author | Sharma, Anil Kumar |
| author_facet | Sharma, Anil Kumar |
| contents | Dynamic pricing systems in logistics have largely borrowed from ride-hailing models designed for urban, short-distance, low-variable-cost environments. These models apply a single surge multiplier to a base fare and treat all vehicles as functionally equivalent. The Indian road freight market invalidates each of these assumptions. India's 14 million commercial trucks operate across a 6.5 million km road network with extreme route diversity, a vehicle mix ranging from 800 kg mini-trucks to 40-tonne trailers with fuel consumption ratios of 1:5.6, and demand patterns dominated by 15+ culturally significant festivals that create predictable but sharp regional supply-demand dislocations. This paper presents the WowTruck Dynamic Pricing Engine, a real-time freight rate optimization system incorporating 17 pricing factors organized across five subsystems: a five-level surge multiplier calibrated to Indian demand elasticity, a festival calendar module covering 15+ festivals with 7-day advance prediction and state-level regional resolution, a vehicle-specific fuel consumption profile linked to live diesel price feeds, a route difficulty premium matrix classifying four Indian road terrain categories, and a 30-day rolling competitor benchmarking loop with win-rate feedback. We describe the implementation architecture, the composite pricing formula, and the market coverage implications. The system represents a materially different class of freight pricing from existing prior art in ride-hailing surge systems, static rate cards, and enterprise TMS platforms such as SAP Transportation Management and Oracle OTM, none of which incorporate festival-driven demand prediction or India-specific vehicle fuel profiles. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19213313 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Multi-Factor Dynamic Pricing for Indian Logistics: Festival Calendar, Vehicle Profiles, and Route Difficulty in Real-Time Freight Rate Optimization Sharma, Anil Kumar artificial intelligence ANKR platform domain AI maritime AI Dynamic pricing systems in logistics have largely borrowed from ride-hailing models designed for urban, short-distance, low-variable-cost environments. These models apply a single surge multiplier to a base fare and treat all vehicles as functionally equivalent. The Indian road freight market invalidates each of these assumptions. India's 14 million commercial trucks operate across a 6.5 million km road network with extreme route diversity, a vehicle mix ranging from 800 kg mini-trucks to 40-tonne trailers with fuel consumption ratios of 1:5.6, and demand patterns dominated by 15+ culturally significant festivals that create predictable but sharp regional supply-demand dislocations. This paper presents the WowTruck Dynamic Pricing Engine, a real-time freight rate optimization system incorporating 17 pricing factors organized across five subsystems: a five-level surge multiplier calibrated to Indian demand elasticity, a festival calendar module covering 15+ festivals with 7-day advance prediction and state-level regional resolution, a vehicle-specific fuel consumption profile linked to live diesel price feeds, a route difficulty premium matrix classifying four Indian road terrain categories, and a 30-day rolling competitor benchmarking loop with win-rate feedback. We describe the implementation architecture, the composite pricing formula, and the market coverage implications. The system represents a materially different class of freight pricing from existing prior art in ride-hailing surge systems, static rate cards, and enterprise TMS platforms such as SAP Transportation Management and Oracle OTM, none of which incorporate festival-driven demand prediction or India-specific vehicle fuel profiles. |
| title | Multi-Factor Dynamic Pricing for Indian Logistics: Festival Calendar, Vehicle Profiles, and Route Difficulty in Real-Time Freight Rate Optimization |
| topic | artificial intelligence ANKR platform domain AI maritime AI |
| url | https://doi.org/10.5281/zenodo.19213313 |